Fighting Fires and Battling the Clock: Advance Care Planning in Family Medicine Residency
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Few family physicians treating patients with life-limiting illness report regularly initiating advance care planning (ACP) conversations about illness understanding, values, or care preferences. To better understand how family medicine training contributes to this gap in clinical care, we asked how family medicine residents learn to engage in ACP in the workplace. METHODS: We coded semistructured interviews with family medicine residents (n=9), reflective memos (n=9), and autoethnographic field notes (n=37) using a constructivist-grounded theory approach. We next used the constant comparative method of grounded theory to develop two composite narratives describing participants' experiences that we then member-checked with participants. RESULTS: We identified six core categories of social process to describe how participants were taught to engage in advance care planning. These social processes included previously unidentified barriers to ACP that were specific to their role as learners. These barriers appeared to lead to cultural avoidance of prognosis, conflation of ACP and goals of care (GOC) conversations, and deferral of difficult conversations to nonprimary care settings. CONCLUSIONS: Family medicine educators should consider developing interventions such as flexible clinic schedules, dedicated ACP time, deliberate observed practice, and structured teaching to address potential barriers identified in this exploratory research. Family medicine leaders may wish to consider directly teaching residents and preceptors about crucial differences between ACP and GOC discussions. Shifting curricular focus toward eliciting values and illness understanding during ACP could help resolve a cultural avoidance of prognosis that limits family medicine residents' attempts to engage in ACP.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".