“I don’t see the whole picture of their health”: a critical ethnography of constraints to interprofessional collaboration in end-of-life conversations in primary care
Bibliographic record
Abstract
Abstract Context Interprofessional collaboration is recommended in caring for frail older adults in primary care, yet little is known about how interprofessional teams approach end-of-life (EOL) conversations with these patients. Objective To understand the factors shaping nurses’ and allied health clinicians’ involvement, or lack of involvement in EOL conversations in the primary care of frail older adults. Methods/setting A critical ethnography of a large interprofessional urban Family Health Team in Ontario, Canada. Data production included observations of clinicians in their day-to-day activities excluding direct patient care; one-to-one semi-structured interviews with clinicians; and document review. Analysis involved coding data using an interprofessional collaboration framework as well as an analysis of the normative logics influencing practice. Participants Interprofessional clinicians (n = 20) who cared for mildly to severely frail patients (Clinical Frailty Scale) at the Family Health Team. Results Findings suggest primary care nurses and allied health clinicians have the knowledge, skills, and inclination to engage frail older adults in EOL conversations. However, the culture of the clinic prioritizes biomedical care, and normalizes nurses and allied health clinicians providing episodic task-based care, which limits the possibility for these clinicians’ engagement in EOL conversations. The barriers to nurses’ and allied health clinicians’ involvement in EOL conversations are rooted in neoliberal-biomedical ideologies that shapes the way primary care is governed and practiced. Conclusions Our findings help to explain why taking an individual-level approach to addressing the challenge of delayed or avoided EOL conversations, is unlikely to result in practice change. Instead, primary care teams can work to critique and redevelop quality indicators and funding models in ways that promote meaningful interprofessional practice that recognize the expertise of nursing and allied health clinicians in providing high quality primary care to frail older patients, including EOL conversations.
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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 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".