Clinicians’ experiences implementing an advance care planning pathway in two Canadian provinces: a qualitative study
Bibliographic record
Abstract
Abstract Background Advance care planning (ACP) is a process which enables patients to communicate wishes, values, fears, and preferences for future medical care. Despite patient interest in ACP, the frequency of discussions remains low. Barriers to ACP may be mitigated by involving non-physician clinic staff, preparing patients ahead of visits, and using tools to structure visits. An ACP care pathway incorporating these principles was implemented in longitudinal generalist outpatient care, including primary care/family medicine and general internal medicine, in two Canadian provinces. This study aims to understand clinician experiences implementing the pathway. Methods The pathway was implemented in one family practice in Alberta, two family practices in British Columbia (BC), and one BC internal medicine outpatient clinic. Physicians and allied health professionals delivered structured pathway visits based on the Serious Illness Conversation Guide. Twelve physicians and one social worker participated in interviews or focus groups at the end of the study period. Qualitative data were coded inductively using an iterative approach, with regular meetings between coders. Results Clinicians described experiences with the ACP care pathway, impact at the clinician level, and impact at the patient level. Within each domain, clinicians described barriers and facilitators experienced during implementation. Clinicians also reflected candidly about potential for future implementation and the sustainability of the pathway. Conclusions While the pathway was implemented slightly differently between provinces, core experiences were that implementation of the pathway, and integration with current practice, were feasible. Across settings, similar themes recurred regarding usefulness of the pathway structure and its tools, impact on clinician confidence and interactions with patients, teamwork and task delegation, compatibility with existing workflow, and patient preparation and readiness. Clinicians were supportive of ACP and of the pathway. Trial registration The study was prospectively registered with clinicaltrials.gov (NCT03508557). Registered April 25, 2018. https://classic.clinicaltrials.gov/ct2/show/NCT03508557 .
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".