Staff Perceptions of a Mealtime Management Educational Video for Training in Long-Term Care Homes
Bibliographic record
Abstract
The purpose of this study was to determine if staff perceive a mealtime management video to be a beneficial and useful training tool in long-term care (LTC) homes. An email invitation was sent to the Dietitians of Canada Gerontology Network inviting dietitians working in LTC homes to participate. A previously used and reliable 25-item questionnaire was used to assess sustained attention/mental effort, learner satisfaction, clinical experience/relevance, and information processing of the video. Dietitians were asked to show the video to LTC staff and distribute the questionnaire to staff after viewing the video. A total of 769 surveys were completed at 28 LTC homes across Canada. Eighty-seven percent (n = 637/736) of participants felt more knowledgeable after viewing the video and 91% (n = 669/738) found the video format easy for learning. Managers had a higher Likert scores (mean = 6.2 out of 7) than continuing care assistant (mean = 5.7, P = 0.02) and food service workers (mean = 5.5, P = 0.001) for the clinical relevance scales. No differences were found for age (χ2 = 5.52, P = 0.60), gender (χ2 = 2.65, P = 0.10), and size of home (χ2 = 3.34, P = 0.34). Staff perceived the video to be useful for their work with residents living in LTC homes and it raised awareness of the importance of their roles at mealtimes.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".