Is there a female-male self-selection bias in TSST-based reactive stress research?
Bibliographic record
Abstract
A selection bias occurs when a given sample of participants only represents a subset of the population under study, which may subsequently limit the generalizability of findings. While previous studies have noticed a potential female-male selection bias in human stress research, with female participants often being over-represented, no prior research has directly addressed this issue in the context of stress reactivity. This exploratory study aimed to systematically examine this observation. A total of 120 scientific articles (N = 10 103) published from 2014 to 2023 on the topic of human stress reactivity retrieved from PUBMED and PsycINFO were examined to compile sex ratios by study location (United States, Germany, China, Canada, Israel, United Kingdom). The meta-analysis and meta-regression results indicated that females participate in reactive stress studies more frequently than males, although the observed difference is small. Moreover, there is no significant discrepancy regarding male and female participation rates between the countries examined. This result supports a higher female representation level in stress research samples. The findings provide leads for future studies aiming to further investigate the underlying antecedents of selection bias in human stress research. A better understanding of the phenomenon could lead researchers to optimize recruitment methods to obtain more representative samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.170 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".