Strategies to facilitate shared decision‐making in long‐term care
Bibliographic record
Abstract
AIM: The aim of this study was to explore shared decision-making among residents, their families and staff to determine relevant strategies to support shared decision-making in long-term care (LTC). BACKGROUND: Meaningful engagement of long-term care home (LTCH) residents and their families in care decisions is key in the provision of quality of care. Shared decision-making is an interprofessional approach to increasing resident and family engagement in care decisions which can lead to higher quality decisions, more relevant care interventions and greater resident, family, and staff satisfaction. Despite these advantages, shared decision-making has not been widely implemented in practice in LTC. METHODS: The study took place in one LTCH in Toronto, Ontario, Canada. A qualitative descriptive design was used to explore how residents, family members and staff described how they collaborate when making decisions concerning resident care, and their perceptions of facilitators and challenges to a collaborative approach to decision-making. Individual interviews were conducted with nine participants: residents, families and staff. Data were analysed using content and thematic analysis. FINDINGS: Four main themes that described resident, family and staff perspectives of shared decision-making were as follows: (a) oral communication pathways for information sharing; (b) supporting resident decision-making autonomy; (c) relational aspects of care facilitate shared decision-making; and (d) lack of effective communication creates barriers to shared decision-making. CONCLUSION: As the demand for LTC continues to increase, it is crucial that healthcare providers engage in collaborative, relational practices that foster high-quality resident care. While a relational approach to care can facilitate shared decision-making, there are opportunities to further cultivate shared decision-making in LTCHs through more effective communication and collaboration. IMPLICATIONS FOR PRACTICE: Understanding how information is shared and decisions are made can facilitate shared decision-making in LTCHs. The strategies identified from this study could be further co-developed and implemented in LTCHs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".