P18 Advance care planning (ACP) discussions: what do they really cost?
Bibliographic record
Abstract
Background Understanding both costs and consequences of ACP programs is important. Available economic analysis have typically reported the consequences but not the prevalence, frequency, duration and with whom ACP discussions take place. Methods We conducted an economic analysis of ACP discussions alongside a trial evaluating ACP videos, across three clinical settings (cancer, heart and kidney disease) and 18 sites in Alberta, Canada. We administered a Health Services Inventory monthly for three months. Participants were asked to recall ACP discussions with professionals from healthcare, legal, financial and spiritual sectors. Results 241 participants (36.1% female; average age, 66 ± 12.2 years) were interviewed at baseline with 95.0% follow-up over the three months. Participants across cancer (n=36), heart disease (n=24), and renal disease (n=40) settings had in total 100 ACP discussions with professionals from healthcare (n= 58), spiritual (n= 14), legal (n=19) and financial (n=9) sectors. The discussions averaged 20.4 minutes and resulted in completion of 16 Goals of Care Designation GCDs, 14 Personal Directives and 9 financial documents. Discussions mostly occurred outside home (n=82, 80.4%) and patients were almost always accompanied by a family member/friend (n=99, 97%). Conclusion(s) Compensating professionals to engage in ACP discussions represents a substantial segment of ACP program cost. Patients and their family/friends also incur costs travelling to and taking time for appointments. Assessing cost-effectiveness of ACP requires program costs in addition to consequences. Patient engagement likewise benefits from understanding the nature and personal costs of these discussions. These data may help professionals advocate for commensurate compensation
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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 | 0.010 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.002 |
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".