Stakeholders’ views on identifying patients in primary care at risk of dying: a qualitative descriptive study using focus groups and interviews
Bibliographic record
Abstract
BACKGROUND: Strategies have been developed for use in primary care to identify patients at risk of declining health and dying, yet little is known about the perceptions of doing so or the broader implications and impacts. AIM: To explore the acceptability and implications of using a primary care-based electronic medical record algorithm to help providers identify patients in their practice at risk of declining health and dying. DESIGN AND SETTING: Qualitative descriptive study in Ontario and Nova Scotia, Canada. METHOD: Six focus groups were conducted, supplemented by one-on-one interviews, with 29 healthcare providers, managers, and policymakers in primary care, palliative care, and geriatric care. Participants were purposively sampled to achieve maximal variation. Data were analysed using a constant comparative approach. RESULTS: Six themes were prevalent across the dataset: early identification is aligned with the values, aims, and positioning of primary care; providers have concerns about what to do after identification; how we communicate about the end of life requires change; early identification and subsequent conversations require an integrated team approach; for patients, early identification will have implications beyond medical care; and a public health approach is needed to optimise early identification and its impact. CONCLUSION: Stakeholders were much more concerned with how primary care providers would navigate the post-identification period than with early identification itself. Implications of early identification include the need for a team-based approach to identification and to engage broader communities to ensure people live and die well post-identification.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".