See It – Do It – Learn It: Learning Interprofessional Collaboration in the Clinical Context
Bibliographic record
Abstract
Background: The primary goal of the Interprofessional Education in Geriatric Care (IEGC) project was to design, deliver, and evaluate interprofessional (IP) clinical placements for pre-licensure learners in geriatric day hospitals.Methods: Project evaluation was guided by the modified Kirkpatrick's Model of Educational Outcomes. Using a controlled before-after design, the Attitudes Toward Health Care Teams Scale (ATHCTS), Team Skills Scale (TSS), and Knowledge Questionnaire were administered to intervention and control learners pre-, post-, and 6 months post clinical placements. Quantitative data were analyzed using descriptive and multivariate statistics. Qualitative data collected through journals and questionnaires were analyzed using content analysis.Findings: Eleven IP clinical placements occurred at 3 test sites involving 32 intervention and 11 control learner participants. There was no significant change, over time, in the ATHCTS quality of care and physician centrality scores for the combined group (i.e., intervention and control) and between intervention and control groups. Time effects were noted in the quality of care scores for the intervention group after controlling for prior IPE (p = .031). The Knowledge scores were higher for the intervention group compared with controls over time (p = .004). Both intervention and control groups demonstrated significant improvements in their TSS scores over time (p = .000), although there was no significant difference in the magnitude of the change between groups (p = .112). Themes observed through qualitative analysis of learners' journals and post-program reflective questionnaires supported the quantitative findings.Conclusions: The IEGC experience was valuable to senior pre-licensure learners in helping them understand collaborative patient-centred practice and team skills. Future research should strive for larger sample sizes through multi-site projects to allow for comparisons within and between clinical sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.013 |
| 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 teacher head, 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".