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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".