INSIGHTS INTO IMPLEMENTING A RELATIONSHIP-CENTRED MEALTIME INTERVENTION FOR CANADIAN LONG-TERM CARE
Bibliographic record
Abstract
Mealtimes are an important aspect of quality of life for residents in long-term care (LTC). Interventions aimed at improving mealtimes often give little attention to the quality of relationships between care staff and residents, and are designed with a single-strategy approach. C.H.O.I.C.E. is a multi-component (i.e., education, training, leadership, communication) intervention to improve relationship-centred care (RCC) and overall mealtime experience for residents in LTC. The current study evaluates the implementation of a proof-of-concept relationship-centred mealtime intervention through understanding the perspectives of participating LTC management and care staff. C.H.O.I.C.E was implemented (April-November 2016) within two units of a LTC home in Southern Ontario, Canada. Data were collected post-intervention using staff and management key informant semi-structured interviews (n=9), which focused on mealtime changes, intervention components, implementation strategies, and program sustainability. Data was analyzed using thematic analysis. Preliminary findings provide several insights into the program and the implementation process: i) Knowing the context and culture to meet staff and resident needs; ii) Getting everyone on board, including management; iii) Keeping communication lines open throughout the process; iv) Sharing responsibility and accountability for mealtime goals and challenges; v) Empowering and supporting staff’s creative mealtime initiatives. Participants identified multiple benefits from the program, most notably an enhanced appreciation for the importance of meals and an increase in meaningful socialization with residents. Our research illustrates the value of C.H.O.I.C.E. and its capacity to improve RCC and residents’ mealtime experiences. Findings will inform the next phase of program development.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".