How do physicians and nurses in family practice describe their care for patients with progressive life-limiting illness? A qualitative study of a ‘palliative approach’
Bibliographic record
Abstract
AIM: To explore how a palliative approach to care is operationalized in primary care, through the description of clinical practices used by primary care clinicians to identify and care for patients with progressive life-limiting illness (PLLI). BACKGROUND: Increasing numbers of people are living with PLLI but are often not recognized as needing a palliative approach to care. To meet growing needs, generalists such as family physicians will need to adopt a palliative approach to care in their own setting. Practical descriptions of a palliative approach in non-specialist settings have been lacking. METHODS: We conducted a qualitative descriptive study design using in-depth semi-structured interviews with 11 key informant participants (6 physicians, 3 nurse practitioners, 1 registered nurse, and 1 registered practical nurse) known to be providing comprehensive care to patients with PLLI in family practices in Ontario, Canada. We asked about their approach to identifying patients with PLLI and the strategies used in their care. We employed content analysis to develop themes. FINDINGS: Participants identified patients by functional decline, change in needs, increased acuity, and the specifics of a condition/diagnosis. Care strategies included concretizing commitment to care, eliciting goals of care, shifting care to the home, broadening team members including leveraging the support of family and community resources, and shifting to a 'proactive' approach involving increased follow-up, flexibility, and intensity. CONCLUSION: Primary care providers articulated strategies for identifying and providing care to patients with PLLI that illuminate an upstream approach tailored to their setting.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".