Examining the associations between mental health, life balance, work-method autonomy, and perceived boundary control among postdoctoral fellows
Bibliographic record
Abstract
High mental health risks and life imbalance among postdoctoral fellows (postdocs) are persistent concerns in academia. However, little is known about the relationship between these two subjects and whether autonomy at work is linked to life balance among postdocs. Online survey responses from 117 postdocs (59% women; 49% non-Hispanic white) were assessed using multiple linear regression analysis to examine whether the work-method autonomy and perceived boundary control of postdocs were linked to life balance. Additionally, logistic regression analysis was used to examine whether postdocs who reported better life balance had lower risks of reporting mood disorder symptoms. We found that 39%, 27%, and 45% of postdocs reported anxiety, depressive, and anxiety-or-depressive disorder symptoms, respectively. Both work-method autonomy and perceived boundary control were positively associated with life balance [B = 0.40, 95% CI = [0.20–0.60]; B = 0.50, 95% CI = [0.32–0.67], respectively]. Postdocs with greater life balance had decreased odds of reporting mood disorder symptoms [anxiety disorder symptoms: adjusted OR = 0.55, 95% CI = (0.37–0.82); depressive disorder symptoms: adjusted OR = 0.31, 95% CI = (0.18–0.55); anxiety-or-depressive disorder symptoms: adjusted OR = 0.42, 95% CI = (0.27–0.65)]. Postdocs' mental health appeared to be influenced by life balance, which may be enhanced by providing work-method autonomy and increasing perceived boundary control.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".