Less money, less care: How nurses in long‐term care allocate hours of needed care in a context of chronic shortage
Bibliographic record
Abstract
The average funding of long-term care in Quebec, Canada, currently covers < 70% of the care hours required, which means that 30% of needs are unmet. The aims of this study were to understand how nurses, when they are in a position to do so, assign care hours, which needs are unmet by care dimensions and whether dimensions with unmet needs vary with client profiles. One-hundred-and-four nurses working in long-term care facilities participated in the study. They filled out individual questionnaires containing three case studies in the form of vignettes. When obliged to cut 30% of the care hours, the nurses ensured that treatment and diagnostic methods were done as prescribed and that vital feeding and elimination functions were preserved. However, they made the choice to cut some mobility and personal-care activities and, especially, communication with patients, families and other professionals. In this, they partly follow the theoretical care prioritization approach of Lefebvre and Dupuis, who take into account the degree of discomfort caused by the situation, the problem's place in Maslow's hierarchy of needs and the availability of a solution. Thus, although the choices made by the nurses follow a logical pattern, they could result in medium-term deterioration in the functional autonomy of their older patients. The overall consequences of these decisions are discussed.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".