The use of systems and organizational theories in the interprofessional field: Findings from a scoping review
Bibliographic record
Abstract
Authors have commented on the limited use of theory in the interprofessional field and its critical importance to advancing the work in this field. While social psychological and educational theories in the interprofessional field are increasingly popular, the contribution of organizational and systems theories is less well understood. This paper presents a subset of the findings (those focused on organizational/systems approaches) from a broader scoping review of theories in the organizational and educational literature aimed to guide interprofessional education and practice. A detailed search strategy was used to identify relevant theories. In total, we found 17 organizational and systems theories. Nine of the theories had been previously employed in the interprofessional field and eight had potential to do so. These theories focus on interactions between different components of organizations which can impact collaboration and practice change. Given the primarily educational focus of the current research, this paper offers new insight into theories to support the design and implementation of interprofessional education and practice within health care environments. The use of these theories would strengthen the growing evidence base for both interprofessional education and practice--a common need for its varied stakeholders.
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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 | 0.053 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.035 | 0.044 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".