Fidelity in workplace mental health intervention research: A narrative review
Bibliographic record
Abstract
The scientific literature on workplace interventions that target individual-level determinants of mental health for primary or secondary prevention is mixed, with many studies failing to show statistically significant, sizeable effects. A methodological characteristic that may explain these mixed findings is fidelity, a multidimensional construct that captures the extent to which an intervention is implemented as intended, in a standardized manner. In this narrative review, we examined the extent to which workplace mental health intervention studies try to enhance or measure the twelve different dimensions of fidelity that have been identified. We conducted comprehensive searches of MEDLINE, Embase, and PsycINFO. Following review, 370 articles were selected for inclusion, of which only 21% explicitly mentioned fidelity. About two-thirds of the articles considered less than half of all relevant fidelity dimensions. Most studies tried to enhance rather than measure fidelity. Only a handful of included studies (n=7, 2%) measured half or more of all relevant fidelity dimensions. Some fidelity dimensions (e.g. theoretical) were considered less often than others (e.g. receipt and enactment). Our review shows that fidelity is insufficiently considered in current workplace mental health literature. We discuss implications for internal and external validity, scalability, and directions for future research.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".