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Get screened! The role of fear and disgust in the
activation of behavioural harm avoidance in medical
settings
Béla Birkás ( bela.birkas@aok.pte.hu )
University of Pécs, Medical School
Botond Kiss
University of Pécs
C. M. Coelho
Azores University
András N. Zsidó
University of Pécs
Research Article
Keywords: blood-injection-injury phobia, perceived control, fear of pain, life history strategies, experience,
knowledge
Posted Date: May 18th, 2022
DOI: https://doi.org/10.21203/rs.3.rs-1648883/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
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Abstract
Although adaptive defence mechanisms are useful in helping us avoid getting injured, they are also
triggered by medical interventions and procedures, when avoidance is harmful. A body of previous results
showed that both fear and disgust play a pivotal role in medical avoidance. However, the underlying
mechanisms are not fully understood. Thus, the aim of the current study was to examine the effects of
experience, perceived control and pain on medical avoidance with disgust and fear as mediating factors
from an evolutionary perspective. We accessed participants’ knowledge of and experience with medical
procedures and former negative medical experiences, lack of health-related information; their life history
strategy variation; pain-related fear and anxiety of medical procedures; perceived control over emotional
reactions and extreme threats; disgust sensitivity; blood-injury-injection phobia and medical treatment
avoidance. We found that more knowledge, experience and a slower life strategy were linked to a greater
level of perceived control and attenuated emotional reactions. Further, better ability to control affective
and stress reactions to negative experiences was linked to reduced disgust and fear of pain, and thus
might mitigate the level of perceived threat, and diminish fear and disgust reactions. Altogether this might
decrease the probability of avoiding medical situations. Implications to treatment are discussed. Results
support the importance of targeting these contextual factors in preventions to increase the likelihood of
people attending regular screenings or seeking medical care when needed.
Introduction
Adaptive defence mechanisms facilitate recognition and appropriate responses to potential
environmental threats that may cause injuries or even death (e.g., Coelho & Purkis, 2009; LeDoux, 2012;
Zsido, et al, 2020). Evolved defensive strategies include learned avoidance behaviours that are aimed to
reduce the probability of close encounters with the individual’s natural enemies (Buck, Weinstein & Young,
2018), pathogens (Kavaliers & Choleris, 2018) and dangerous situations in general(Coelho & Balaban,
2015). Most current research has as basic premise that adaptations and selection mechanisms build
physiological and behavioural traits to enhance the adjustment of the organism to the environment in
which they live. Life History Theory (LHT) (Kaplan & Gangestad, 2005) provides a framework to describe
different tness maximizing strategies, such as this renement (West & Gardner, 2013). According to LHT,
animals (including humans) have to balance the needs to allocate energy to survival and reproduction as
well as to the actual organismic conditions and environmental context (Dunkel, et al., 2021; Bjorklund &
Ellis, 2014; Kaplan & Gangestad, 2005). For example, if an infection is present, the immune system is
activated, requiring substantial energy (e.g., fever; see Straub, 2012). Hence, the energy expenditure
regulation and the behavioural strategies of the individual change during this period. Unpredictable
conditions favour the utilization of faster LHT strategies, whereas more predictable conditions facilitate
the activation of slower LHT strategies (Kaplan & Gangestad, 2005). Faster life strategies can be
characterized by an elevated sensitivity to reward and more impulsive behaviours, while slower life
strategies are more future oriented, adopting controlled behaviours (Birkás, Csathó, Gács & Bereczkei,
2015; Del Giudice, Gangestad & Kaplan, 2015).
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Avoidance strategies to pathogens and injuries include mechanisms associated with fear and anxiety
(e.g., predation or injury risk), and disgust (Buck, Weinstein, & Young, 2018; Weinstein, Buck & Young,
2018; Coelho et al., 2020). Conceptualizing fear as a defensive response to eminent threats and anxiety
as response to potential threats (Lang, Davis & Öhman, 2000), these notions share similarities with
disgust propensity. As suggested by the
disease-avoidance model
(Matchett & Davey, 1991), disgust is an
adaptive response to sources of potential contaminants and pathogens, protecting from infectious
diseases (Oaten, Stevenson & Case, 2009; Tybur, Lieberman, Kurzban & DeScioli, 2013). Both direct
encounters with natural enemies and indirect cues associated with various sources of risks (e.g., without
the actual presence of enemies) can activate physiological mechanisms and behavioural strategies of
avoidance leading to certain tness costs to the individual through physiological (e.g., stress) and
behavioural (e.g., aversion of certain places) effects (see Doherty & Ruehle, 2020). The neurobiological
basis of approach and avoidance behaviour, suggested by the revised Reinforcement Sensitivity Theory
(Gray and McNaughton, 2000; McNaughton and Corr, 2004) consists of three overlapping, but different
affective-motivational systems. The
Fight-Flight-Freeze System
(FFFS) is the primary system responsible
for active avoidance and escape behaviours in response to fear, thus, it is sensitive to aversive stimuli,
both unconditioned (innate) or conditioned (learned). The activation of the FFFS is associated with a
desire to escape and an emotional state corresponding to anxiety and fear. At the behavioural level the
FFFS is expressed in active avoidance and escape. Together, these are related to panic and phobia on a
psychopathological level (Smillie, Pickering & Jackson, 2006).
Medical interventions and procedures carry the possibility of violating the integrity of the body and
increase the likelihood of infections; similarly, to getting injured. Fear of blood, injury and injections (BII)
can also affect individuals’ motivation to either seek or procrastinate medical help and care (Birkás et al.,
2021; Dubayova et al., 2010). These medical fears are associated with excessive distress towards stimuli
commonly present in medical settings (e.g.: vaccination, symptoms of illness, medical devices, see: Page,
1994; Peeters, Bennett, Donoghue, Kennelly & Kenny, 2020) as well as disgust-related responses (see
Cisler, Olatunji & Lohr, 2009). Distress and disgust associated to medical situations often result in
avoidance, even when medical treatment is needed (Pollock, 2016). Being one of the most common
specic phobias, BII-related fears have the most serious potential consequences to health affecting a
large number of people (Birkás et al., 2021; Kleinknecht et al., 1999). The evolved avoidance mechanisms
to deal with potential contamination can also be triggered when they should not. The disease-avoidance
model, indeed, describes the functional role of disgust in the avoidance of contamination and protection
of the individual against infections (Matchett & Davey, 1991; Olatunji et al., 2014). Accordingly, a large
body of studies demonstrated that disgust is a critical factor in medical fear related disorders (i.e., BII
phobia; see Knowles, Jessup & Olatunji, 2018 for a review) and, consequently, underscored the relevance
of this reaction in understanding medical avoidance. Medical care avoidance not only increases
morbidity and mortality risk associated with treatable and preventable health conditions (Caplan, 2014;
Spleen, Lengerich, Camacho & Vanderpool; 2014), but it also intensies negative psychological outcomes
such as anxiety and depression (Manne, Glassman & Du Hamel, 2001; Rosenthal, Hall, Palm, Batten &
Follette, 2005), and elevates healthcare-related economic costs. Treatment avoidance is suggested to be
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affected by both external (e.g., socioeconomic or demographic) and internal (i.e., knowledge, experiences,
pain sensitivity) factors (Abraham & Sheeran, 2015; Taber, Leyva, & Persoskie, 2015; Kannan, & Veazie,
2014; Simon et al., 2022).
One of the most straightforward signals of medical need and, at the same time, one of the most powerful
aversive stimuli is pain. Consequently, humans are characterized by a vigilance to pain, that expedites
escape and avoidance behaviour (Crombez et al., 2005)., and is closely related to fear (Panksepp, Sacks,
Crepeau & Abbott, 1991). Pain is crucial for survival due to its signal function alerting the individual to
potential damages and activating behavioural responses to prevent or limit subsequent harms or injuries
(Johansen & Fields, 2004). Perception of pain is modulated by a wide range of environmental and
psychological factors including previous experiences, socioeconomic factors, education, health-literacy,
stressful life events, anxiety, and fear (Castelnuovo & Schreurs, 2019). Uncontrollability and helplessness
are also dominant elements of fears and phobias and are strongly linked to avoidance (American
Psychiatry Association [APA], 2013). Accordingly, the personal appraisal of pain and its potential
consequences affects not only the perception of pain, but the associated avoidant behavioural responses
as well. Perceived lack of control related to painful experiences elevate the negative orientation towards
pain (i.e., pain catastrophizing; see Simon et al., 2022). This intensies negative expectation regarding
subsequent pain-related experiences and results in evaluating potential consequences of pain as more
threatening and potentially harmful. Anticipation of potential threats increases the level of anxiety and
may even induce disgust, and, hence, facilitates the development of pain-related fear (Markfelder & Pauli,
2020; Turk & Wilson, 2010). To sum up, the way how individuals interpret pain, and its potential
consequences affects fear of pain (Algophobia) and related defence behaviours, such as avoidance
enabling the person to memorize and later avoid cues associated with pain.
Health-related knowledge and former medical experiences may alter the effects of helplessness and
uncontrollability in both negative and positive ways.
Fear-avoidance models
suggest that fear of pain is
an important component of pain chronication and related disabilities (Markfelder & Pauli, 2020).
Individuals suffering from pain commonly experience feelings of helplessness and uncontrollability,
eliciting emotions of embarrassment or shame fuelled by individual and interpersonal worries (e.g., being
unable to work; being a burden for the family) (Karos, Williams, Meulders & Vlaeyen, 2018).
Uncontrollability and helplessness are also key determinants of cognitive pain processing and have
detrimental physical and psychological health consequences (Samwel, Evers, Crul & Kraaimaat, 2006).
Negative experiences or lack of understanding of symptoms enhances the level of perceived lack of
control and vulnerability. However, a large body of previous evidence show that positive outcomes of
former medical care and higher level of health literacy are associated with increased sense of control and
resilience (Schieman & Plickert, 2008; Wulff, Donato & Lurie, 2015). Health related knowledge and
medical experiences were also found to affect severity of chronic illnesses, procrastination of health-care
utilization and biopsychosocial consequences of symptoms (Abraham & Sheeran, 2015; Taber, Leyva, &
Persoskie, 2015; Kannan, & Veazie, 2014).
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Hence, the overarching goal of the current study was (1) to examine the effects of medical fear and fear
of pain on medical avoidance with (2) disgust sensitivity as mediating factor, whilst controlling for former
experiences, level of medical knowledge, perceived control and life-history strategies. Former studies
overlooked the evolutionary aspects of medical avoidance or did not include both, fear and disgust into
the examination, or lack of information related to participant’s medical knowledge and/or experiences. To
prevent the severe consequences of medical avoidance, the identication of relevant risk factors of
medical avoidance and exploring the interplay between disgust and fear is important. More specically,
based on the literature reviewed, we hypothesised that former negative medical experiences, lack of
health-related knowledge, exaggerate medical fears, and fear of pain will trigger individuals to perceive
medical situations more threatening and potentially harmful, which, in turn, will activate avoidance.
Furthermore, disgust will enhance perceived threat, causing a biased interpretation for fear-related stimuli
and overestimation of the potential negative consequences of the situation.
Methods
Participants
We recruited 906 Caucasian participants (233 males, 662 females, 11 preferred not to answer), aged 18–
68 years (M = 24.83, SD = 7.87) through the Internet by posting invitations on various forums and mailing
lists to obtain a heterogeneous sample. The required sample size for this experiment was determined by
computing estimated statistical power with a conservative approach (RMSEA = .035, 1-β = .95, df = 103)
using the semPower package for R (Moshagen and Erdfelder, 2016; R Core Team, 2020). The analysis
indicated a required a minimum total sample size of 458; thus, our study was adequately powered. The
data were collected in January to March 2021. Regarding COVID-19 regulations, there was no curfew at
the time of the data collection and medical examinations were freely available for everyone. The
participants lled out the questionnaires online, using Google Forms, on a voluntary basis. None of them
reported having a psychiatric disorder. Additionally, we deleted three invalid entries (two duplication and
one fake), thus they were not analysed and are not included in number of participants or mean age. The
research was approved by the Hungarian United Ethical Review Committee for Research in Psychology
and was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration
of Helsinki). Informed consent was obtained from all participants.
Materials
Demographic information
Demographic questions included age, gender, inquiries about experience and knowledge of medical
procedures, examinations, and injuries. We accessed participants
knowledge
of medical procedures,
examinations, and injuries with questions about their healthcare-related studies (e.g., rst aid, nursing,
medical doctor) and jobs in a healthcare-related eld. Participants
experience
with medical procedures,
examinations, and injuries were assessed with questions about how often they go to examinations,
screenings, blood draws, blood plasma donation, receive injections; how often they see serious injuries or
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blood in person or in the media (e.g., movies, Internet); whether they ever had a serious injury; and whether
they had to provide care for a relative for at least one month.
Life history strategy variation
To indicate the adaptive strategies of the individual shaped by avoidance-related experiences and
associated tness costs during childhood, we used the Mini-K scale of the Arizona Life History Battery
(Figueredo et al., 2006), a brief 20-item measuring fast–slow life history strategies. Participants
responded on 7-point Likert-type scales, mean scores were calculated across all items where higher
scores indicated a slower life strategy. In this study, McDonald’s omega was .75.
Fear of Pain
To asses pain-related fear and anxiety of medical procedures, we used the Fear of Medical/Dental Pain
(FPQ MD) scale of the brief Fear of Pain Questionnaire (McNeil et al., 2018). Participants responded on 5-
point Likert-type scales, sum scores were calculated across all items, higher scores indicated higher levels
of fear. In this study, McDonald’s omega was .76.
Perceived Control
We used the Anxiety Control Questionnaire (ACQ) to measure perceived control over emotional reactions
and extreme threats (Brown, White, Forsyth & Barlow, 2004). The ACQ is a 15-item questionnaire that has
three subscales: emotion control, threat control, and stress control. Participants responded on 6-point
Likert-type scales, sum scores were calculated for each subscale, higher scores indicated higher levels of
perceived control. In this study, McDonald’s omegas were .81 (emotion), .78 (threat), and .74 (stress).
Disgust sensitivity
To assess disgust sensitivity, we used the revised version of the Disgust Scale-Revised (DSR) (Olatunji et
al., 2007). The DSR has 25 items and taps into three dimensions of disgust: core disgust, animal
reminder disgust (AR), and contamination-based disgust. From the 25 questions, 13 are true–false items
(scored 0 or 1) and 12 items are rated on 3-point Likert-type scales scale referring to the extent to which
participants nd a given experience disgusting (i.e., not, slightly, very; scored 0, 0.5, 1, respectively). Mean
scores were calculated for each subscale, higher scores indicated higher levels of disgust sensitivity. In
this study, McDonald’s omegas were .63 (core), .60 (animal reminder), and .61 (contamination-based).
Medical Fears
The short version of the Medical Fear Survey (MFS-short) is a 25-item questionnaire measuring medically
related fears across ve domains: injections and blood draws (IBD), sharp objects (SO), blood (BL),
mutilation (MU), and examinations and symptoms (ES) (Olatunji et al., 2012). Participants rated the items
using 4-point Likert-type scales. We calculated sum scores for each subscale, higher scores indicated
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higher levels of fear. In this study, McDonald’s omegas were .88 (injections and blood draws), .83 (sharp
objects), .87 (blood), .82 (mutilation), and .79 (examinations and symptoms).
Avoidance
We measured the extent to which participants have avoided medical treatment due to fear of various
procedures and anticipated outcomes with two subscales (fear of nding a serious illness and fear of
injections and pain) of the Medical Avoidance Survey (MAS) (Kleinknecht et al., 1996). The MAS has 21
items, rated on 5-point Likert-type scales. We calculated sum scores for each subscale, higher scores
indicated higher levels of avoidance. In this study, McDonald’s omegas were .85 (serious illness) and .84
(injections and pain).
Data analyses
First, we computed knowledge and experience scores separately but with the same method. We
calculated the z scores for each variable and then added them up. The z transformation was necessary
because not all questions were rated on the same length scale.
We performed a Structural Equation Modelling using the JASP statistical software version 0.15 for
Windows (JASP Team, 2020) utilizing the lavaan (v. 0.6-1) package for R (Rosseel, 2012) to assess t
measures for our proposed models. Questionnaires with multiple scales (ACQ, DS, MFS, and MAS) were
entered as latent variables. We used the diagonally weighted least squares (DWLS) estimator (Bandalos,
2014) with standard error calculation, standard model test; missing data handling was listwise deletion
(zero cases were removed). To evaluate model t, we used the relative chi-square (Χ2/df), the comparative
t index (CFI), the Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA) with
90% condence intervals, and the standardized root mean square residual (SRMR). The cut-offs for good
model t were a relative chi-square < 5 (Kline, 1998), CFI and TLI values of .95 or greater (Hu and Bentler,
1998), RMSEA and SRMR values of .08 or lower (Browne and Cudeck, 1992).
Results
The test yielded a good model t (Χ2(100)=343.28, p<.001, CFI=.97, TLI=.95, RMSEA = .05, 90%CI = [.046
– .058], SRMR = .06). See Figure 1 for the model tested. FPQ MD was negatively predicted by knowledge
(β = -.17, p < .001) and experience (β = -.08, p = .032), but not by MiniK (β = -.05, p = .13). ACQ scores were
positively predicted by Mini-K (β = .26, p < .001), the routes from knowledge (β = .05, p = .12) and
experience (β = .05, p = .12) were non-signicant. DSR scores were positively predicted by FPQ MD (β =
.41, p < .001) and Mini-K (β = .20, p < .001), while experience (β = -.19, p < .001) and ACQ (β = -.32, p <
.001) were negative predictors. Knowledge (β = -.05, p = .26) did not have a signicant effect on DSR.
MFS was positively predicted by FPQ MD (β = .27, p < .001) and DSR (β = .69, p < .001), while knowledge
(β = -.10, p = .010) predicted it negatively. The paths from experience (β = .004, p = .93), MiniK (β = -.01, p
= .83) and ACQ (β = -.05, p = .31) were non-signicant. MAS scores (R2 = .29) were predicted positively by
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MFS (β = .24, p < .001) and FPQ MD (β = .25, p < .001), while ACQ (β = -.17, p < .001) was a negative
predictor.
-------------------------------
Figure 1 about here
-------------------------------
Regarding covariances, ACQ and FPQ MD (β = -.33, p < .001), and knowledge and experience (β = .28, p <
.001) scores showed strong covariances as expected. We also controlled for a covariance between two of
the DSR subscales, animal remainder and contamination (β = .19, p < .001). Table 1 shows the
descriptive statistics of the sample on all measures used; Supplementary Table 1 shows the
unstandardized coecients and condence intervals. See Supplementary Table 2 for correlational
coecients between the variables included in the model.
--------------------------------------
Table 1 about here
-------------------------------------
Discussion
It has been proposed that adaptive defence mechanisms facilitate recognition and appropriate
responses, such as avoidance behaviours to potential environmental threats to reduce the probability of
getting injured (Coelho & Purkis, 2009; LeDoux, 2012; Zsido, et al, 2020). However, the avoidance
mechanisms may also be triggered by medical interventions and procedures, when avoidance is more
harmful. BII-related fears are associated with excessive distress towards stimuli commonly present in
medical settings (Peeters et al., 2020), are highly common (Birkás et al., 2021), and has the most serious
potential consequences (Kleinknecht et al., 1999) as they often result in the procrastination of seeking
medical help (Dubayova et al., 2010). Therefore, the aim of our study was to evaluate the impact of BII-
related fears and disgust on medical avoidance with respect to individual differences in perceived control,
medical knowledge, former experiences and life-history strategies. Our results showed that more
knowledge of and experience with medical settings, and slower life strategies were associated with higher
levels of perceived control and less intense emotional reactions. Better ability to control affective and
stress reactions to negative experiences was linked to reduced disgust sensitivity and lower levels of fear
of pain. These factors might mitigate the level of perceived threat, diminish fear and disgust reactions,
and ultimately, might result in a decreased probability of avoiding medical screenings, examinations and
care. This is in line with the disease-avoidance model (Matchett & Davey, 1991), Reinforcement
Sensitivity Theory (Gray and McNaughton, 2000; McNaughton and Corr, 2004), and fear-avoidance
models (Markfelder & Pauli, 2020) drawing a complex picture about the underlying mechanisms and
motivations related to the avoidance of medical settings.
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Our results underline the importance of examining the role of contextual factors, showing that slower life-
history strategies were associated with elevated sense of control and disgust sensitivity. That is, more
future oriented and less impulsive behavioural strategies are connected to increased behavioural control
and more intense vigilance towards environmental cues of pathogens indicating stronger disease
avoidance reactions. Similarly, medically relevant experiences and knowledge were associated with
decreased disgust sensitivity and both fear of pain and medical fears, suggesting that direct and perhaps
indirect encounters with environmental threats shape the frequency of avoidance reactions. These
ndings are in line with existing models of behavioural defence mechanisms (Doherty and Ruehle, 2020;
Gray & McNaughton, 2000; Oaten et al., 2009) and support the notion, that avoidance behaviour is
triggered by individual evaluation processes based on the interpretation of former and actual
circumstances. Further, we also found that perceived control of emotional reactions and threats were
connected to medical avoidance through direct and indirect pathways, that is, better control was linked to
a lesser likelihood of medical avoidance and reduced disgust sensitivity and fear of pain. It has been
posited that better ability to control affective and stress reactions to negative experiences alters pain
processing (Simon et al., 2022) and therefore, mitigates the level of perceived threat, resulting in
diminished fear and disgust reactions (Crombez et al., 2005; Markfelder & Pauli, 2020). However, in
medical settings perceived control might be decreased and its inuence on avoidance less articulated due
to the potential defencelessness of the individual (Samwel et al., 2006).
Regarding disgust sensitivity, our ndings suggest that different aspects of disgust may reect a shared
vulnerability for medical fears and avoidance. Accordingly, proneness to disgust may be characterized by
harm avoidance, in general, and can be considered as a relevant trigger for medical avoidance in
particular. This is in line with the disease-avoidance model that describes the functional role of disgust in
the avoidance of contamination and protection against infections (Matchett & Davey, 1991; Olatunji et al.,
2014). These results are in agreement with former studies and show that disgust, as a response to
potential health-related threats, affects avoidance behaviour in a different way compared to fear
(Knowles, et al., 2018; Olatunji, et al., 2014). Previous research suggests that people with BII phobia report
greater disgust sensitivity than non-phobics, that exposure to stimuli related to medical and settings and
BII increases disgust more for phobic compared to non-phobic participants, and that disgust returns more
rapidly after extinction than fear (Olatunji et al., 2009). As suggested by the disgust law of contagion of
magical belief systems, things that were once in contact always retain that connection (Rozin et al.,
1986). Disgust intensies the perception of the likelihood of negative or harmful consequences
(Krusemark & Li, 2011) elevating both the level of fear and probability of avoidance in medical settings.
Further, fear of pain and medical fears directly impact medical avoidance, which link was supported by
several former studies as well (Asmundson & Taylor, 1996; Buer & Linton, 2002; Peeters et al., 2020).
Hence, reducing medical avoidance might be a constant battle against the reoccurrence of beliefs and a
strong urge of avoiding contamination and paint. Slower LHT might be useful in helping an individual to
see the long-term payoff of a situation rather than the immediate danger.
Besides its signicant contributions, the current study also has a few limitations. Longitudinal data would
have been more informative (e.g., by measuring how avoidance behaviour changes over time), and there
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might be other indicators that contributed to the probability of avoiding medical care. Further work is
needed to explore and validate the different behavioural aspects of disgust and fear on medical
avoidance and to analyse age-related effects in more detail. Further, external factors such as the form of
medical care or severity of former injuries and diseases probably affect medical avoidance might alter
the connections but were not evaluated in the current study. Age may be also associated with the quality
and severity of medical experiences, fear, and avoidance, but our study could not reveal this connection
as the age-range of our sample was not wide enough. Furthermore, the effects of the internal factors
included in our study on actual avoidant behaviours should be claried in the practice, e.g., in real
medical settings.In sum, internal contextual factors (reected in experiences, knowledge, sense of control,
disgust and fear) affect individual medical avoidance tendencies. Thus, it is crucial to reect to these
factors in order to enhance the motivation of people to regularly attend medical screenings and seek
medical help if needed. Elevated avoidance spoils the adherence and favours the development of fear-
related disorders (e.g., phobias) together with distortions in perceptual and decisional processes. Despite
the health-related importance of pain and medical interventions, these biased cognitive-affective
processes may prohibit the individual to invoke adequate help because of the activation of avoidance as
part of the behavioural harm avoidance. Thus, an adaptive behavioural strategy might produce a
maladaptive outcome in causing more harm to the individual than good.
Declarations
Ethics approval and consent to participate.
The research was approved by the Hungarian United Ethical Review Committee for Research in
Psychology (Ref. Nr.:EPKEB 2019-121) and was carried out in accordance with the Code of Ethics of the
World Medical Association (Declaration of Helsinki). Informed consent was obtained from all
participants.
Consent for publication
Not applicable.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author
on reasonable request.
Competing interests
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The authors declare that they have no competing interests.
Funding
C.M.C was supported by the Center for Psychology at the University of Porto, Portuguese Science
Foundation (FCT UIDB/00050/2020). ANZS was supported by the NKFI “OTKA” PD-137588 grant and the
ÚNKP-21-4 New National Excellence Program of the Ministry for Innovation and Technology from the
source of the National Research, Development and Innovation Fund.
Authors' contributions
B.B.:
Conceptualization, Methodology, Writing – original draft & editing, Writing - review & editing,
B.K.
:
Investigation,
C.M.C.
: Writing – original draft & editing, Writing - review & editing,
A.N.Zs.:
Formal analysis,
Funding acquisition, Methodology, Writing – original draft & editing, Writing - review & editing
Acknowledgements
Not applicable.
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Tables
Table 1 – Descriptive statistics (Means and Standard Deviations – SD) of the sample on
Disgust sensitivity (DS) core, animal remainder (AR), contamination; Medical Fear Survey
(MFS) injection and blood draw (IDB), sharp objects (SO), examinations and symptoms
(ES), blood (BL), mutilation (MU); Fear of Medical/Dental Pain (FPQ MD); Life history
strategy variation (Mini-K); Perceived Control (ACQ) emotion (EC), threat (TC), and stress
(SC); Medical Avoidance (MAS); Age; Number of respondents (separately for genders).
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Mean SD
Experience 4.149 1.122
Knowledge 1.649 1.078
MiniK 5.086 0.697
FPQ_MD 7.872 3.103
ACQ_EC 12.486 5.514
ACQ_TC 18.533 5.348
ACQ_SC 7.369 3.308
DSR_core 0.501 0.149
DSR_AR 0.460 0.221
DSR_contam 0.395 0.199
MFS_IBD 2.550 3.142
MFS_SO 1.480 2.301
MFS_ES 7.301 3.497
MFS_BL 2.099 2.955
MFS_MU 7.286 4.065
MAS_SI 5.397 5.967
MAS_BI 2.930 3.870
Age 24.83 7.87
Gender 233
662
11
male
female
did not answer
Figures
Figure 1
Our proposed model on the connection and background mechanisms of avoidance of health care due to
fear and anxiety. Squares indicate measured variables; ellipses indicate latent variables (comprised of the
subscale of the questionnaire). All pathways implemented in the Structural Equation modelling are
presented on the gure. All reported estimates are the maximum likelihood standardized point-estimates.
Statistically signicant unstandardized point estimates are indicated with a star (* p < .05, ** p < .001).
Signicant positive predictors are indicated with red colour, signicant negative predictors are indicated
with blue colour. Nonsignicant routes are indicated by dashed lines and grey colour.