Stakeholders’ views on identifying patients in primary care at risk of dying: a qualitative descriptive study using focus groups and interviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it