Patients’ Stories of Interprofessional Whole-Person Care
Bibliographic record
Abstract
Objectives: Interprofessional whole-person care has been depicted as being able to enhance patient health outcomes, increase health care provider satisfaction with care delivery, lower health care spending and decrease wait times for receiving care. Limited research has been conducted into exploring patients’ experiences of being recipients of this type of care. The objective of this oral presentation is to disseminate findings of a Master of Nursing study through patients’ stories of experience receiving care on aunit where inter professional care is practiced.Methods: Three participants underwent a two step data collection process: a one hour semi-structured interview and a 30 minute symbolic image artistic exercise, as adapted from Schwind’s Narrative Reflective Process. Participants were invited to describe how they experienced receivingcare from an interprofessional team and whether or not they believed whole-person care was delivered to them. Collected data are being analyzed using Clandinin and Connelly’s Narrative Inquiry approach of three dimensionalspace, temporality, sociality and place.Results: The emerging results suggest that participants express satisfaction with the care they received from the interprofessional team on their unit. Their stories indicate that strong interprofessional team-work can contribute to patient satisfaction in care received. For these teams to be successful, from the patients’ point of view, there needs to be: better communication between care providers, greater involvement of the patient in decision making, proper identification of who comprises the teams, andconsistency in team composition.Conclusion: By acknowledging experiences and feelings ofpatients who have received care from an interprofessional team, there is potential to increase sustainability of these teams. The data generated through this study can potentially help health care providers, who are members of interprofessional teams, to deliver more effective, comprehensive whole-personcare within health care institutions.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 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".