Through Teaching Are We Learning? Learning Through Teaching: Facilitating Interprofessional Education Experiences
Bibliographic record
Abstract
Background: Despite a growing recognition of the value of collaborative patientcentred practice (CPCP) there is a lack of evidence identifying key elements and approaches to an effective interprofessional (IP) education intervention for clinical team members. The present study was conducted to address the paucity of rigorous mixed methods research to address the question: Does clinician team facilitation and mentorship of senior pre-licensure learners participating in IP clinical placements improve team members' attitudes, knowledge, skills, and perceived behaviours in CPCP?Methods: Based on the assumption that Geriatric Day Hospital clinical teams were already highly collaborative, educational experiences for clinical team members were not designed a priori. Rather, the educational experience was grounded in Mezirow's transformative learning theory, proposing that learning is a process of becoming aware of one's assumptions and revising these assumptions based on critical self-reflection. The option to participate in structured observation and feedback by an external observer using the Team Observation Scale provided important and unique opportunities for team reflection. Using the Controlled Before and After (CBA) design, the Attitudes Toward Health Care Teams Scale (ATHCTS), Team Skills Scale (TSS), and Knowledge Questionnaire were administered pre- and post-clinical placements to intervention and control groups. Data were analyzed by descriptive, bivariate, and repeated measures ANOVA. Qualitative data (evaluation and self-reflective forms) were analyzed using content analysis techniques.Results: Eleven IP clinical placements at 3 sites occurred between January 2007 and March 2008 (intervention N = 48; control N = 7). There was no significant change over time between intervention and control groups for the ATHCTS Quality of Care or Physician Centrality subscale scores, the TSS scores, or the Knowledge scores. Qualitative results suggested that participants were more aware of IP teaming, reflective of their own practice, and reported making changes in their own practice and mentorship of students as a result of their engagement in the study.Conclusions: This study demonstrated the viability of using structured observation and feedback processes as a reflective learning exercise. Further research is required to help identify key approaches and elements to an effective IPE intervention in clinical practice.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".