Development of a decision guide to support the elderly in decision making about location of care: an iterative, user-centered design
Notice bibliographique
Résumé
For the elderly to get the care and services they need, they may need to make the difficult decision about staying in their home or moving to another home. Many other people may be involved in their care too (friends, family and healthcare providers), and can support them in making the decision. We asked informal caregivers of elderly people to help us develop a decision guide to support them and their loved ones in making this decision. This guide will be used by health providers in home care who are trained to help people make decisions. The guide is in French and English. To design and test this decision guide we involved elderly people, their caregivers and health administrators. We first asked them what they needed for making the decision, and then designed a first version of the guide. Then we asked them to look at it and give feedback, which was used to make the final version. We then used scientific criteria to check its content and the language used. The final decision guide was acceptable to the caregivers, their elderly loved ones, and the health administrators. The guide is currently being evaluated in a large research project with home care teams in the province of Quebec. Background As they grow older, many elderly people are faced with the difficult and preference-sensitive decision about staying in their home or moving to a residence better adapted to their evolving care needs. We aimed to develop an English and French decision aid (DA) for elderly people facing this decision, and to involve end-users in all phases of the development process. Methods A three-cycle design with involvement of end-users in Quebec. End-users were elderly people (n = 4) caregivers of the elderly (n = 5), health administrators involved in home-care service delivery or policy (n = 6) and an interprofessional research team (n = 19). Cycle 1: Decisional needs assessment and development of the first prototype based on existing tools and input from end-users; overview of reviews examining the impact of location of care on elderly people’s health outcomes. Cycle 2: Usability testing with end-users, adaptation of prototype. Cycle 3: Refinement of the prototype with a linguist, graphic designer and end-users. The final prototype underwent readability testing and an International Patient Decision Aids (IPDAS) criteria compatibility assessment to verify minimal requirements for decision aids and was tested for usability by the elderly. Results Cycle 1: We used the Ottawa Personal Decision Guide to design a first prototype. As the overview of reviews did not find definitive evidence regarding optimal locations of care for elderly people, we were not able to add evidence-based advantages and disadvantages to the guide. Cycle 2: Overall, the caregivers and health administrators who evaluated the prototype (n = 10) were positive. In response to their suggestions, we deleted some elements (overview of pros, cons, and consequences of the options) that were necessary to qualify the tool as a DA and renamed it a “decision guide”. Cycle 3: We developed French and English versions of the guide, readable at a primary school level. The elderly judged the guide as acceptable. Conclusion We developed a decision guide to support elderly people and their caregivers in decision making about location of care. This paper is one of few to report on a fully collaborative approach to decision guide development that involves end-users at every stage (caregivers and health administrators early on, the frail elderly in the final stages). The guide is currently being evaluated in a cluster randomized trial. Trial registration: NCT02244359.
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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 | 0,021 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».