Patient and caregiver perspectives on early identification for advance care planning in primary healthcare settings
Bibliographic record
Abstract
BACKGROUND: As part of a broader study to improve the capacity for advance care planning (ACP) in primary healthcare settings, the research team set out to develop and validate a computerized algorithm to help primary care physicians identify individuals at risk of death, and also carried out focus groups and interviews with relevant stakeholder groups. Interviews with patients and family caregivers were carried out in parallel to algorithm development and validation to examine (1) views on early identification of individuals at risk of deteriorating health or dying; (2) views on the use of a computerized algorithm for early identification; and (3) preferences and challenges for ACP. METHODS: Fourteen participants were recruited from two Canadian provinces. Participants included individuals aged 65 and older with declining health and self-identified caregivers of individuals aged 65 and older with declining health. Semi-structured interviews were conducted via telephone. A qualitative descriptive analytic approach was employed, which focused on summarizing and describing the informational contents of the data. RESULTS: Participants supported the early identification of patients at risk of deteriorating health or dying. Early identification was viewed as conducive to planning not only for death, but for the remainder of life. Participants were also supportive of the use of a computerized algorithm to assist with early identification, although limitations were recognized. While participants felt that having family physicians assume responsibility for early identification and ACP was appropriate, questions arose around feasibility, including whether family physicians have sufficient time for ACP. Preferences related to the content of and approach to ACP discussions were highly individualized. Required supports during ACP include informational and emotional supports. CONCLUSIONS: This work supports the role of primary care providers in the early identification of individuals at risk of deteriorating health or death and the process of ACP. To improve ACP capacity in primary healthcare settings, compensation systems for primary care providers should be adjusted to ensure appropriate compensation and to accommodate longer ACP appointments. Additional resources and more established links to community organizations and services will also be required to facilitate referrals to relevant community services as part of the ACP process.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".