CREATION AND EVALUATION OF A VIDEO SERIES SHARING A PALLIATIVE APPROACH TO CARE IN CANADIAN LONG-TERM CARE HOMES
Bibliographic record
Abstract
Abstract Video-assisted patient education is a favored format for accessing healthcare information. Based on a consultative process, the purpose of this project was to create and evaluate a video education resource to support communication between staff and family caregivers in Canadian long-term care (LTC) homes when introducing a palliative approach to care. The development of the resource proceeded in four stages: consulting with lived experience advisors; refining the concept; piloting the concept; and finalizing the resource. The concept included a voice-over-infographic introduction plus a four-part video series highlighting fundamental aspects of a palliative approach in LTC and featuring personal narratives by a resident, family caregiver, employee, and manager. During pilot research, family caregivers and LTC staff (n=16) participated in focus group discussions and surveys to assess the acceptability of the resource and its impact on knowledge acquisition. They also considered potential uses of the resource. After reviewing the introduction and a full video, as well as the concepts for the three remaining videos, the average acceptability of the video series across fourteen 5-point Likert-type items was 4.49 (SD=0.40) for family caregivers and 4.66 (SD=0.35) for LTC staff. The average positive change in palliative care knowledge on a 100-point scale was 6.94 (SD=10.56) for family caregivers and 12.5 (SD=10.76) for LTC staff. Overall, this work contributes a resource with high content and format acceptability, and good potential for a positive impact on knowledge about a palliative approach. It offers useful considerations for introducing the resource to staff and family caregivers.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".