Evaluating the Conversation Starter Kit in Long Term Care: A Canadian Perspective
Bibliographic record
Abstract
Abstract This study evaluated an advance care planning intervention, the Conversation Starter Kit (CSK) booklet, for use in long term care (LTC) homes. This study used a quasi-experimental, one group pre/post design. Quantitative surveys were administered before and after a 3-month advance care planning intervention (CSK booklet). Data were collected at three LTC homes in southern Ontario. We collected data from 55 resident who were able to make decisions on their own paired with 11 family members of these residents. We also collected data from 24 family members of residents who were not able to make decisions on their own. Quantitative surveys were administered before and after the intervention. An additional structured interview was completed at the end of the intervention period, which included both closed and open-ended questions to assess perceptions about the CSK booklet’s use or non-use. Residents reported higher engagement in advance care planning after having completed the CSK booklet than before, particularly related to asking questions to health care providers about health care decisions. Family members reported feeling very certain that they would be able to make decisions on behalf of the resident but they felt less certain after completing the CSK booklet, implying that the CSK booklet raised their awareness of the types of decisions that they might need to make, hopefully triggering them to become more prepared for these decisions in the future. The CSK appears acceptable, easy to use for residents and family members/friends in LTC, and can improve resident engagement 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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".