A Web-Based Well-being Program for Health Care Workers (Thrive): Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mental health has come to be understood as not merely the absence of mental illness but also the presence of mental well-being, and recent interventions have sought to increase well-being in various populations. A population that deserves particular attention is that of health care workers, whose occupations entail high levels of stress, especially given the ongoing COVID-19 pandemic. A neuroscience-based web-based well-being program for health care workers-the Thrive program-has been newly developed to promote habits and activities that contribute to brain health and overall mental well-being. OBJECTIVE: This paper describes the protocol for a randomized controlled trial whose objective is to evaluate the Thrive program in comparison with an active control condition to measure whether the program is effective at increasing well-being and decreasing symptoms of psychological distress in health care workers at a designated Australian hospital. METHODS: The trial will comprise two groups (intervention vs active control) and 4 measurement occasions over a 12-week period. A survey will be administered in each of weeks 0, 4, 8, and 12, and the well-being program will be delivered in weeks 1-7 (via web-based video presentations or digital pamphlets). Each of the 4 surveys will comprise a range of questionnaires to measure well-being, psychological distress, and other key variables. The planned analyses will estimate group-by-time interaction effects to test the hypothesis that mental health will increase over time in the intervention condition relative to the active control condition. RESULTS: The Thrive program was delivered to a small number of wards at the hospital between February 2021 and July 2021, and it will be delivered to the remaining wards from October 2021 to December 2021. A power calculation has recommended a sample size of at least 200 participants in total. A linear mixed model will be used to estimate the interaction effects. CONCLUSIONS: This trial seeks to evaluate a new web-based well-being program for health care workers at a major public hospital. It will contribute to the growing body of research on mental well-being and ways to promote it. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000027819; https://tinyurl.com/58wwjut9. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34005.
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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 | 0.053 | 0.051 |
| Meta-epidemiology (narrow) | 0.009 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.007 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.110 | 0.020 |
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".