A Multilevel Study of Patient-Centered Care Perceptions In Mental Health Teams
Bibliographic record
Abstract
Abstract Background: The combination of interprofessional collaboration in teams and patient-centered care is a necessary amalgamation when it comes to delivering complex mental healthy care and services. Yet collaboration is challenging and patient-centered care is intricate to manage. This study examines the impact of predictors of patient-centered care such as team adaptivity and proactivity, collaboration, belief in interprofessional collaboration, informal role self-efficacy in multidisciplinary mental health teams.Method: Cross-sectional multilevel design using self-administered bilingual validated questionnaires.Results: This study showed that belief in interprofessional collaboration’s impact on patient-centered perceptions is increased in teams with high collaboration. We also showed that collaboration is a mediator; that is, a process by which team adaptive and proactive behaviors are transformed into positive patient-centered perceptions.Conclusions: In terms of research our results are in line with recent theorising on team processes and specifically established collaboration as key in a multilevel examination of predictors of patient-centered care perceptions. In terms of practice, we showed that multidisciplinary teams should know that working hard on collaboration as an answer to the complexity of patient-centered care impacts the teams’ ability to respond to its challenges but also impacts individuals’ beliefs central to the delivery of interprofessional care.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".