Developing a data-informed care planning improvement intervention in long-term care in Nova Scotia: protocol for an advisory-led interpretive qualitative study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,090 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,009 | 0,006 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,005 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,046 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».