Capturing the Active Ingredients of Multicomponent Participatory Organizational Stress Interventions Using an Adapted Study Design
Bibliographic record
Abstract
Adapted study designs use process evaluation to incorporate a measure of intervention exposure and create an artificial control and intervention groups. Taking into account exposure levels to interventions combines process and outcome evaluation and strengthens the design of the study when exposure levels cannot be controlled. This study includes longitudinal data (two assessments) with added process measures at time 2 gathered from three complex participatory intervention projects in Canada in a hospital and a university. Structural equation modelling was used to explore the specific working mechanisms of particular interventions on stress outcomes. Results showed that higher exposure to interventions aiming to modify tasks and working conditions reduced demands and improved social support, but not job control, which in turn, reduced psychological distress. Exposure to interventions aiming to improve relationships was not related to psychosocial risks. Most studies cannot explain how interventions produce their effects on outcomes, especially when there are multiple concurrent interventions delivered in several contexts. This study advances knowledge on process evaluation by using an adapted study design to capture the active ingredients of multicomponent interventions and suggesting some mechanisms by which the interventions produce their effects on stress outcomes. It provides an illustration of how to conduct process evaluation and relate exposure levels to observed outcomes. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".