The Development and Preliminary Evaluation of the “SPA‐LTC Voices” Video Series as a Means of Articulating a Palliative Approach in Long‐Term Care Settings
Bibliographic record
Abstract
Palliative care has earned its place as a respected approach to medicine that focuses on quality of life, symptom management, a team approach, and family involvement, typically following the diagnosis of a life‐limiting illness. To improve health equity, it is important to encourage the adaptation of palliative care practice and philosophy beyond hospices and within a range of care settings. To support further adaptation in long‐term care, our research team Strengthening a Palliative Approach in Long‐Term Care (SPA‐LTC) created the video education resource “SPA‐LTC Voices” to explore what a palliative approach entails in a long‐term care context and to dispel persistent myths about palliative care. After consulting with palliative care experts and family caregivers, we designed a four‐part series using a storytelling approach (i.e., presenting accounts of lived experience) within a three‐act narrative structure (i.e., setup, tension, and resolution). We then employed an embedded intervention mixed methods design to pilot‐test the acceptability of the video series and the outcome of knowledge transfer during structured interviews with 16 participants, who were either family caregivers (12) or healthcare providers (4). Integrated qualitative and quantitative findings confirmed potential for positive impact on knowledge transfer across both audiences, including an improved understanding of the values and practices involved in palliative care. Integrated findings also confirmed high acceptability of the narrative format and the diversity of the storytellers. Overall, this pilot research suggests that the “SPA‐LTC Voices” video series holds promise as a tool to support education within long‐term care settings.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".