Providing person-centred mealtime care for long term care residents with dementia
Bibliographic record
Abstract
Person-centred care is a holistic care approach that aims to build up and support the personhood of residents with dementia, and thereby enhance quality of life. Through a review of the literature on mealtimes in long term care homes, four main aspects of person-centred mealtime care were identified: providing food choices and preferences, supporting residents’ independence, promoting the social side of eating, and showing respect. Using a critical realist lens, this descriptive qualitative study examined current implementation of person-centred mealtime care, the influences on its implementation, and steps to more fully adopt a person-centred approach. Semi-structured interviews were conducted with 52 staff from four diverse long term care homes in southern Ontario. Participants included frontline workers, registered health care professionals, and managers. Interviews were transcribed and analysed for themes. A conceptual framework was developed through analysis of the interview data, identifying five key ways to support staff to provide person-centred care: forming a strong team, working together to provide care, enabling staff to know the residents better, equipping staff with a toolbox of strategies, and creating flexibility to optimize care. Specific strengths and areas for improvement in implementation of person-centred mealtime care were identified and explained using this conceptual framework. Elements of the framework were also applied to explain important considerations for hiring staff, educating and training staff, developing a culture of good teamwork, and involving family members and volunteers in mealtime care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".