Bibliographic record
Abstract
Interprofessional care (IPC) has been discussed in the literature as having the ability to lower health care expenditures, decrease wait times, enhance patient health outcomes and increase healthcare provider (HCP) satisfaction with care-delivery. To date, limited research has been conducted on patients’ experiences of receiving IPC. Using Connelly and Clandinin’s Narrative Inquiry qualitative research approach, three participants were invited to engage in a modified version of Schwind’s Narrative Reflective Process, a creative self-expression tool that utilizes storytelling, metaphor selection, drawing, creative writing and reflective dialogue. Participants shared their stories, and selected and drew metaphors that best represent for them their experiences of receiving IPC. They were also asked whether or not they believe person-centered care was delivered to them. Collected stories were analyzed as per the three common places of Narrative Inquiry: temporality, sociality and place, as well as the three levels of justification: personal, practical and social. Told stories were examined through the theoretical lens of the National Canadian Interprofessional Competency Framework. Three narrative threads emerged within this study: communication, interprofessional team composition, and patient within interprofessional team. The findings appear helpful to inform educators, HCP, policy makers, and researchers, as they strive to enhance person-centered interprofessional care practice. For patients, a clear opportunity for their voices to be heard has been outlined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".