The conversation around malnutrition: a qualitative study of dietitian and patient perspectives
Bibliographic record
Abstract
Malnutrition is prevalent among older adults in Canada and it can be mitigated through personalized dietitian-led counselling. This qualitative study aimed to explore how malnutrition is communicated and perceived, providing insight for future care. This multisite qualitative study was conducted in Alberta and Quebec, Canada. Participants were recruited through purposive sampling to target patients ≥65 years old who spoke French or English, had a malnutrition diagnosis, and received counseling for malnutrition from a dietitian. Dietitians who had worked directly with patients ≥65 years old with a diagnosis of malnutrition were recruited through advertisement. Semi-structured interviews were recorded both in person and over the phone. Data were transcribed verbatim and analyzed using reflexive thematic analysis. Twenty-five patients and 10 dietitians were interviewed, producing five themes (two dietitian themes and three patient themes). First, diagnosing malnutrition: dietitians highlighted challenges in diagnosing malnutrition due to limited support and resources. Second, using the “ M” word: dietitians hesitated to use the term “malnutrition” with patients. Third: knowing I'm malnourished: patients reported not being informed of their diagnosis. Fourth, what is malnutrition? Patients revealed the word sounds extreme and unrelatable. Reactions to the diagnosis varied: acceptance, shock, detached, and neutral. Finally, stigma and blame: some patients distanced themselves from the stigma by rationalizing their nutrition problems. Gaps in management of malnutrition were identified. Future research should focus on how to communicate the diagnosis to improve outcomes for malnutrition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".