Developing a data-informed care planning improvement intervention in long-term care in Nova Scotia: protocol for an advisory-led interpretive qualitative study
Bibliographic record
Abstract
Introduction The quality of care provided in long-term care (LTC) homes has been a concern for many years, and the COVID-19 pandemic has further raised awareness of this issue. Care planning helps identify and prioritise areas to improve LTC residents’ health. Data are routinely collected to support care planning, for example, the interRAI LTC facilities instrument and real-time location systems. However, the best way to use these data to inform care planning and decision-making while including residents and family members remains elusive. This study aims to develop a data-informed care planning improvement intervention that uses routinely collected data to guide resident-centred care planning in LTC. Specifically, we will: (1) examine how, where and why routinely collected data are used in current care planning processes in LTC; (2) identify barriers and facilitators to using data to guide care planning from the perspectives of staff, residents and family caregivers; and (3) develop care planning intervention guided by the Behaviour Change Wheel. Methods and analysis An advisory committee of residents, family members and LTC staff will provide study oversight of this interpretive qualitative description study, conducted in LTC homes in Nova Scotia from May 2023 to April 2025. Participants, including LTC residents, their family members and staff, will be invited to participate in two 60–90 min focus groups or 45–60 min individual interviews and/or three 2-hour observation sessions. Data from interviews, focus groups and care observations will be analysed using inductive content analysis to answer the objectives. Next, we will deductively map the identified barriers and facilitators onto the Behaviour Change Wheel, which suggests that C apability, O pportunity and M otivation are needed for a Behaviour to occur (COM-B system). Subsequently, we will have a 1 day advisory committee meeting to: (1) select the intervention components using the APEASE criteria, which asks whether the function is A ffordable, P racticable, E ffective, A cceptable, S afe, and promotes E quity; and (2) describe the final intervention using the Template for Intervention Description and Replication checklist to ensure the reproducibility of the intervention in future work. The result of this study has the potential to contribute to the understanding of the process in enhancing care and resident outcomes in LTC homes across Canada. Ethics and dissemination This study has been approved by the Dalhousie University Health Sciences Research Ethics Board. Informed consent will be obtained from all participants or their substitute decision-makers before they take part in interviews, focus group discussions and care observations. Data will be de-identified, and privacy and confidentiality will be maintained through secure storage and handling of both electronic and physical documents. Study findings will be shared with participants through lay summaries and infographics after the second interview and observation, as well as at the conclusion of the study. Results will also be disseminated to researchers, healthcare professionals and LTC providers across Canada via presentations at local, national and international conferences, publications in open-access journals and through print and video materials tailored to the audience.
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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 | 0.090 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".