Supports and barriers to person-centred planning in the community-care sector: A qualitative analysis of three perspectives
Notice bibliographique
Résumé
Background: The community-care sector provides in-home and community-based health and social service support for individuals with intellectual and developmental disabilities, physical disabilities, or complex medical needs. Person-centred planning (PCP) has been associated with high-quality service provision and improved quality-of-life outcomes in community-care populations. However, there is limited literature on the facilitators and barriers impacting the PCP process in the community-care sector. Objective: This study aims to identify supports and barriers to the PCP process in community-care organizations. By assessing current needs and identifying relevant supports for implementation, we aim to provide insight on how to optimize community-based health and social service delivery to ensure equitable outcomes for all persons-supported (i.e., persons receiving care). Methods: We collaborated directly with knowledge users at each stage of the process (i.e., study design, interview guide development, recruitment, knowledge translation, etc.) through our formal research partnership with PHSS, a not-for-profit community-care organization based in a large urban city in Ontario, Canada. This analysis combines information from three data sources and includes perspectives from persons-supported, as well as organizational and sector-wide perspectives. We conducted a total of 42 semi-structured interviews, 18 with persons-supported at PHSS, 1 with a family member of a person-supported, 11 with frontline staff at PHSS, and 12 with program executives across different community-care organizations in Southwestern Ontario, Canada. We asked participants about the PCP process at their organization and the supports and barriers that facilitate or hinder the provision of care. We relied on PHSS staff as intermediaries in interviews with persons-supported who requested or required further support. We analyzed the data thematically, using a pragmatic, qualitative, descriptive approach. Results: We identified four key themes on factors impacting the PCP process: (1) the health and capacity of the person-supported; (2) sector challenges; (3) integration between sectors; and (4) community connections and inclusion. Participants described how the health and capacity of the person-supported impacts the PCP process, particularly for individuals who have mental health challenges or difficulty communicating. Adequate funding and staffing were described as sector challenges. Funding was regularly identified as a barrier, while a skilled and dedicated workforce was described as a key facilitator. Executives highlighted ongoing staffing shortages widespread across the sector, and their impact on the quality of care provision. Participants described a need for increased integration with other care sectors including acute care systems. Strong connections with community members or organizations were central to the PCP process, although participants also described inclusion and accessibility barriers (e.g., attitudinal, transportation-related). Conclusions: This study provides valuable insight into the factors impacting the PCP process in community-care organizations. By identifying barriers—individual health-related, sector-wide funding and staffing challenges, integration needs, and the importance of community—it offers lessons to community-care organizations, policymakers and service providers around the globe. These findings highlight the importance of addressing challenges through targeted intervention strategies designed to optimize the PCP process and ensure equitable outcomes across diverse community-care populations, contributing to international efforts aimed at enhancing care delivery.
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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 | 0,020 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,012 | 0,011 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».