The association between task interdependence and participation in decision-making: a moderated mediation model in mental healthcare
Bibliographic record
Abstract
Participation in decision-making is crucial to healthcare workers collaborating across professions. Important correlates of participation in decision-making include task interdependence, informational role self-efficacy, and beliefs in the benefits of interprofessional collaboration. We hypothesised that although task interdependence is directly related to participation in decision-making, the relationship is mediated by informational role self-efficacy. Beliefs in the benefits in interprofessional collaboration act as a mediator. A sample of 315 mental healthcare workers answered validated questionnaires. Conditional processing was used to test the moderated mediation. Generally, the results confirmed our hypotheses. There was a direct relationship between task interdependence and participation in decision-making and it was mediated by informational role self-efficacy, and both relationships depend on whether healthcare workers believe in the benefits of interprofessional collaboration. However, although the moderation effect of beliefs in the benefits of interprofessional collaboration between task interdependence and informational role self-efficacy was positive, the moderation effect was negative for the relationship between task interdependence and participation in decision-making. Although there is an inherent logic in the positive relationships that were found, the negative moderation might be explained by the contrast between the structural view and the volitional view of task interdependence.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".