Conversations that matter: using videos to model quality ACP conversations between clinicians and patients
Bibliographic record
Abstract
In 2008, the “Advance Care Planning: Goals of Care Designation (ACP/GCD) (Adult)” policy was systematically implemented across all sectors within Alberta Health Services, Calgary Zone. A critical component for successful implementation of the policy was clinician development of skills with engaging in quality advance care planning (ACP) conversations. Pre and post policy evaluation has consistently identified ongoing barriers with initiating and engaging in the quality conversations required for successful outcomes with policy adoption. Key challenges reported by clinicians are lack of confidence and competency with approaching patients for the purpose of engaging in ACP conversations. Clinicians frequently identify similar themes found in ACP literature; fear of diminishing hope, lack of skills in initiating and sustaining conversations and fear of causing harm to patients in this process. This presentation will present a series of clinician education videos modelling key components of ACP conversations. The videos provide clinicians with a ‘tool box’ of phrases and questions they might consider saying in different situations or at different stages of the process of conversations, and provide clear examples of communication techniques such as clarifying, maintaining eye contact and open body language. These videos can be used as a basis for role modelling-based education for all clinicians to practice and improve communicating about advance care planning. Additionally, utilising videos as a teaching methodology can assist with timely and flexible education reducing some of the barriers with accessing skill development and training.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".