Identifying resident care areas for a quality improvement intervention in long-term care: a collaborative approach
Bibliographic record
Abstract
BACKGROUND: In Canada, healthcare aides (also referred to as nurse aides, personal support workers, nursing assistants) are unregulated personnel who provide 70-80% of direct care to residents living in nursing homes. Although they are an integral part of the care team their contributions to the resident care planning process are not always acknowledged in the organization. The purpose of the Safer Care for Older Persons [in residential] Environments (SCOPE) project was to evaluate the feasibility of engaging front line staff (primarily healthcare aides) to use quality improvement methods to integrate best practices into resident care. This paper describes the process used by teams participating in the SCOPE project to select clinical improvement areas. METHODS: The study employed a collaborative approach to identify clinical areas and through consensus, teams selected one of three areas. To select the clinical areas we recruited two nursing homes not involved in the SCOPE project and sampled healthcare providers and decision-makers within them. A vote counting method was used to determine the top five ranked clinical areas for improvement. RESULTS: Responses received from stakeholder groups included gerontology experts, decision-makers, registered nurses, managers, and healthcare aides. The top ranked areas from highest to lowest were pain/discomfort management, behaviour management, depression, skin integrity, and assistance with eating. CONCLUSIONS: Involving staff in selecting areas that they perceive as needing improvement may facilitate staff engagement in the quality improvement process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".