Healthcare aide involvement in team decision‐making in long‐term care: A narrative review of the literature
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To provide an overview and synthesis of the current evidence on healthcare aides' involvement in team decision-making in long-term care. BACKGROUND: Healthcare aides provide the most direct care to residents in long-term care homes and are uniquely positioned to influence the quality of care. Yet, they are not typically included in team decisions for improving resident care. As demand for long-term care increases, it is essential that we have a comprehensive understanding of ways to support healthcare aides' role on the interprofessional team for decision-making about resident care. DESIGN: Narrative review. METHOD: Five electronic databases were searched for articles published in English between 2008 and 2020. Thematic analysis was conducted to synthesise findings using an organising framework. Reporting followed the PRISMA-ScR. RESULTS: Twelve studies were included. Results indicate that work environment factors that influenced (supported or hindered) healthcare aides' involvement in decision-making included information access/availability, hierarchical staffing structures and supervisor support/shared governance. Relational processes that influenced team decision-making included team communication and collaboration, information sharing and exchange, and the quality of work relationships among team members. Strategies are discussed that could address the identified barriers and support healthcare aides' active involvement in team decisions regarding resident care. CONCLUSIONS: This review highlights the pervasive underutilization of healthcare aides, who have the most knowledge of residents to support person-centred care. There remains a paucity of research on healthcare aides' involvement in team decision-making. Research is needed to examine the effectiveness of interventions to support healthcare aides' participation in decision-making and the impact on staff and resident outcomes. RELEVANCE TO CLINICAL PRACTICE: It is crucial that healthcare aides are afforded opportunities to be part of the interprofessional team for information sharing and decision-making for resident care. Managers play a key role in supporting healthcare aides' inclusion in decision-making.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".