Peeling the layers: a grounded theory of interprofessional co-learning with residents of a homeless shelter
Bibliographic record
Abstract
Clients, patients, families, and communities must be conceived as partners in care delivery, not just as recipients (D'Amour, D. & Oandasan, I. (2005). Journal of Interprofessional Care, 19(Suppl.), 8-20). Health-care students need an opportunity to understand community member self-determination, partnership, and empowerment (Scheyett, A., & Diehl, M. ( 2004 ). Social Work Education, 23(4), 435-450), within the frame of interprofessional education (IPE) where community members are involved as teachers and learners. The aim of this grounded theory research was to determine the conditions that support health-care students to learn with, from, and about community members. This study took place in a shelter for the homeless where nursing and social work students learned interprofessionally along with residents and clients of the shelter. Data were gathered through 7 months of participant observation, interviews, and focus groups. The interprofessional co-learning theory that emerged introduces the three phases of entering, engaging, and emerging, which co-learners experienced at different levels of intensity. This article outlines the conditions that support each of these phases of the co-learning process. This interprofessional co-learning theory provides a basis for further development and evaluation of IPE programs that strive to actively include community members as teachers and learners, experts, and novices together with service providers, students, and faculty members.
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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 | 0.035 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".