Examining the effects of interprofessional education on mental health providers: Findings from an updated systematic review
Bibliographic record
Abstract
BACKGROUND: Interprofessional education (IPE)'s popularity as an effective strategy to enhance the ability of health professionals to work in interprofessional teams has grown substantially over the past decade. AIMS: Building upon the work of Reeves ( 2001 ), this paper provides an updated systematic review of the effects of IPE on mental health providers delivering adult mental health care from 1967 to 1998. METHOD: A systematic review was undertaken to update an earlier review in this field. Three databases (Medline, CINAHL, and PsycINFO) were searched from January 1999 to December 2007, and 16 articles were included in the review. RESULTS: A triangulation approach was used to rate the quality of the evidence reported by the studies, and yielded the following article ratings: five good, five acceptable, four poor, and two unacceptable. Overall, the use of theory to inform IPE was limited. Methodologically, before-and-after study designs were most common, as were multiple data collection techniques. Few studies attributed negative/unintended consequences to IPE, or reported clear limitations to their approaches or findings. CONCLUSION: The review suggests an improvement in the methodological rigor in research designs, with a preference for mixed methods and outcomes measured at more complex levels.
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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 | 0.021 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".