Meal context and food offering in Quebec public nursing homes: the perspectives of first-generation immigrant residents, family members, and frontline care aides
Bibliographic record
Abstract
Purpose The purpose of this paper is to gain a better understanding of the meal context and the food offering in Quebec public nursing homes for non-autonomous seniors, particularly with respect to first-generation immigrants. Design/methodology/approach A focused ethnography approach was used. Semi-structured interviews were conducted with three distinct groups: non-Quebec-born residents (n=26), their families (n=24) and frontline care staff (n=51). Structured non-participative observations were made in facilities. Findings First-generation immigrants, however, long ago they arrived in Quebec, adapted with difficulty and often not at all to the food offering. Resident’s appetite for food offer was a problem for reasons related primarily to food quality, mealtime schedules, medication intake, physical and mental condition, and adaptation to institutional life. Family/friends often brought in food. Care staff tasks were becoming increasingly tedious and routinized, impacting quality of care. Practical implications Institutions should render procedures and processes more flexible and adapt their food offering to the growing diversity of their client groups. For residents, the meal experience is profoundly transformed in nursing homes in terms of form, conditions, rituals and meaning. A better understanding of lived situations shaped by a more refined cultural sensitivity would go a long way toward achieving a better quality of life not only for residents but also for their families and friends. Care aides, on whose shoulders rests the responsibility of ensuring that meals are safe and pleasant moments for socializing and maintaining social dispositions, are ambivalent about their work. Originality/value The paper is based on an original study. To the authors’ knowledge, the literature on the meal context and food offering in Quebec public nursing homes, regardless of population type, was non-existent. Analyzing and interpreting the results by crossing the discourses of immigrant residents, their family and friends, and frontline care staff made it possible to reveal different aspects of the phenomenon, which, if considered together, shed light on the meal context in public nursing homes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".