Re‐examining the evaluation of interprofessional education for community mental health teams with a different lens: understanding presage, process and product factors
Bibliographic record
Abstract
This paper revisits the formative evaluation of a pilot project that offered in-service interprofessional education (IPE), which is designed to enhance the collaborative practice, to two UK community mental health teams (CMHTs). While the IPE was well received and resulted in some improvements in team functioning, wider successes were elusive. Specifically, collaborative action plans were not implemented, and the pilot programme was ultimately not rolled out to other CMHTs. The purpose of this paper is to test the usefulness of the presage-process-product (3P) framework for analysis as a means to untangle the complex web of factors that promoted and inhibited success in this initiative. The framework, which captures key features of the initiative as a dynamic system, proved effective, yielding new insights, making connections clearer and highlighting the critical importance of presage. We argue that use of the 3P model during the development of in-service IPE could ensure that planning oversights are minimized, thereby improving outcomes.
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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 | 0.084 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".