Using Shared Decision-Making Resources in Long-Term Care: a Qualitative Study
Bibliographic record
Abstract
Background: Shared decision-making (SDM) incorporates people's individual preferences and context into individualized, person-centred decisions. Persons living in long-term care (LTC) should only take medications that are a good fit for them as individuals. Methods: We conducted a pilot study to understand experiences of two LTC homes in Ontario as they tested implementing SDM resources to support medication decisions. LTC homes conducted two Plan-Do-Study-Act (PDSA) cycles supported by an Advisory Group composed of LTC home representatives and stakeholders involved in resource design. Rapid qualitative analysis of transcripts and field notes from Advisory Group meetings elucidated how SDM resources were used. Results: Each site was positively engaged but implemented resources differently. The pharmacist and physicians at Site 1 introduced proton-pump inhibitor (PPI) deprescribing as their primary intervention, identifying suitable residents, informing residents and families of the deprescribing process, and providing selected SDM resources to residents, caregivers and staff. Representatives reported limited engagement with SDM resources and difficulty measuring the impact of PPI deprescribing. Representatives from Site 2 disseminated the SDM resources to residents and caregivers for use at care conferences and focused on front-line staff education and involvement. This site reported that some residents/caregivers were interested in participating in SDM and using the resources, while others were not. The impact of the resources on SDM at this site was unclear. Conclusions: Within the context of LTC, further research is needed to clarify the meaning and importance of SDM in medication decision-making. Implementation of SDM will likely require a multi-faceted approach.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 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, 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".