Randomized trial of personalized psychological feedback from a longitudinal online survey and simultaneous evaluation of randomized stepped wedge availability of in-person peer support for hospital staff during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: We tested if automated Personalized Self-Awareness Feedback (PSAF) from an online survey or in-person Peer Resilience Champion support (PRC) reduced emotional exhaustion among hospital workers during the COVID-19 pandemic. METHOD: Among a single cohort of participating staff from one hospital organization, each intervention was evaluated against a control condition with repeated measures of emotional exhaustion at quarterly intervals for 18 months. PSAF was tested in a randomized controlled trial compared to a no-feedback condition. PRC was tested in a group-randomized stepped-wedge design, comparing individual-level emotional exhaustion before and after availability of the intervention. Main and interactive effects on emotional exhaustion were tested in a linear mixed model. RESULTS: Among 538 staff, there was a small but significant beneficial effect of PSAF over time (p = .01); the difference at individual timepoints was only significant at timepoint three (month six). The effect of PRC over time was non-significant with a trend in the opposite direction to a treatment effect (p = .06). CONCLUSIONS: In a longitudinal assessment, automated feedback about psychological characteristics buffered emotional exhaustion significantly at six months, whereas in-person peer support did not. Providing automated feedback is not resource-intensive and merits further investigation as a method of support.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".