Is there a female-male self-selection bias in TSST-based reactive stress research?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it