Individualized participatory care planning for individuals with intellectual and developmental disabilities: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: Goal setting for persons within health and social care environments can be a challenging task; although health and social care settings aim to address a person's care needs, the literature tends to focus on health. Person-centred care should encompass the goals/needs/wants of the person, whether these goals focus on career, relationship, and/or health domains. To understand how a person-centred participatory goal setting process is carried out in a care environment, we used an integrated knowledge translation approach. METHODS: We conducted 11 semi-structured interviews with community-care staff to understand a person-centred planning process, including key components and impacts. RESULTS: The interviews provide a thorough understanding of an implemented approach to person-centred plans, including its creation, implementation, and benefits (for the person-supported, family, friends, and staff). Person-centred plans provide a map with which to plan activities based on a persons' goals, interests, and capacities, and have positive impacts for the person-supported, family, friends, and staff. CONCLUSIONS: Our study highlights how a community-care organization can facilitate person-centred services through person-centred plans and has implications for wider uptake of person-centred plans in community-care organizations.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 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".