Interdisciplinary shared governance: A literature review
Bibliographic record
Abstract
Objective: Interdisciplinary shared governance (IDSG) is important in healthcare to achieve quality and excellence in patient care. Initially adopted in healthcare facilities for the nursing discipline, the recent trend is to expand it to include other disciplines. This paper examined the factors that affect interdisciplinary collaboration that effect successful implementation of an IDSG model.Methods: A literature review on SG, interdisciplinary collaboration, and factors that may potentially influence its successful implementation was conducted.Results: The review of the literature identified several factors grouped under three major themes that affect IDSG. The first theme was individual factors that include the subthemes provider attitude, beliefs, interpersonal skills, and status quo. The second theme was shared factors that includes both individual as well as organizational factors. They include physician-nurse relationships, clear goals and vision, motivation, trust and respect, and team functional skills. Finally, the organizational factors refer to those that impact the working environment and influence decision making by members of the various interdisciplinary teams. These factors include organizational structure, organizational culture, leadership, education, resources, professional boundary, and role ambiguity.Conclusions: The IDSG requires that groups in different disciplines make informed decisions pertaining their work environment as well as those towards patient’s 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".