Supports and barriers to person-centred planning in the community-care sector: A qualitative analysis of three perspectives
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".