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 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 | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".