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Record W6906677131 · doi:10.17605/osf.io/9gqu2

Stress reactivity and recovery to psychosocial stress in the laboratory as a predictor of reactivity to daily-life stressors.

2021· other· en· W6906677131 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsTrier social stress testStressorStress (linguistics)Stress measuresAffect (linguistics)Reactivity (psychology)Heart rateHeart rate variabilityFight-or-flight response

Abstract

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Psychosocial stress tasks, such as the Trier social stress test (TSST) and the Montreal imaging stress test (MIST) induce mild stress in a controlled environment ideally suited to study the stress response (Mortensen & Cialdini, 2010). These tasks typically result in a physiological stress response, characterized by changes in the autonomous nervous system (ANS) such as increased heart rate (HR) and skin conductance level (SCL), and decreased heart rate variability (HRV), but also changes in affect such as an increase in negative affect (NA). Findings from studies on stress tasks are used to make inferences on how individuals respond to stress in their natural environment. However, given the “artificial” nature of such tasks (Klein et al., 2012; Mortensen & Cialdini, 2010), the extent of their ecological validity remains unclear. In other words, it is not known if these findings are in fact related to reactivity to daily-life stressors. To date, ecological validity has mostly focused on comparing the ANS response to a stress task with either a measure of variability (e.g. standard deviation, minimum, maximum values) in a 24h recording (Gerin et al., 1998; Kamarck et al., 2003; Pollak, 1991; Turner et al., 1994), or alternatively the response to a similar pre-specified stressor in daily-life such as giving a presentation, or engaging in marital conflict (Baucom et al., 2018; Gerin et al., 1998; Johnston, Tuomisto, & Patching, 2008). Arguably, these methods are either too broad to capture stress, or on the contrary too restrictive to represent the general stress response, which may in turn limit their validity. Ideally, stress tasks should reflect reactivity to daily-life stressors in general rather than to a single specific stressor. Likewise, validity should extend from the ANS response (i.e. heart rate, blood pressure) to also cover affective reactivity, which has largely been neglected. Experience sampling methodology (ESM) may be the solution to both of these limitations. ESM is a diary method where participants receive a set number of assessments at random times during the day, for multiple days. Assessments consist of various self-report items on affect, behavior, context, and appraisals (Myin-Germeys et al., 2018; Vaessen et al., 2017). ESM captures affective changes in daily-life with minimal intervention, and as such may be ideally suited to measure the response to daily-life stressors in general. To our knowledge, ESM has yet to be used to assess the ecological validity of a stress task. In time-contingent sampling with ESM, assessment occurs independent of stress onset, meaning that stress occurs sometime between the current and the previous assessment. This in turn suggests that rather than capturing peak-stress, it likely captures the response already in the recovery phase, when measures are returning to baseline. For this reason, it is possible that we are actually capturing recovery in daily-life rather than reactivity. Consequently, laboratory recovery may be a better predictor of daily-life “reactivity” than laboratory reactivity. The current study aims to test the ecological validity of the repeated Montreal Imaging Stress Task (rMIST), a psychosocial stress task where participants are under the illusion to be competing against another participant on an arithmetic task (Dedovic et al., 2005). More specifically, we assess whether reactivity and recovery to the rMIST are associated with general daily-life stress reactivity for all measures of affect (i.e. NA), and ANS reactivity (i.e. HR, RMSSD, and SCL).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.008
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0060.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.348
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueOpen Science FrameworkFrench-language works237,207