Observe, Coach, Assist, Report: A personal support practice framework to build Home and Community Care workforce capacity in Ontario, Canada
Bibliographic record
Abstract
Background: In Canada, most paid home care is provided by an unregulated workforce of personal support (PS) providers. Given their frequent and consistent interactions with clients, PS providers are well-positioned to identify unaddressed client needs; however little infrastructure exists to communicate these observations with the broader home care team. To support deeper integration of PS providers and their contributions into home care teams, the Observe, Coach, Assist and Report (OCAR) Framework was collaboratively developed in 2015 with point-of care clinicians and practice leaders. Following development, the OCAR Framework was adopted to guide PS practice at a large Canadian homecare agency, but little is known about how OCAR is used in care planning and decision-making at micro, meso, and macro levels. Aims: Since development of the OCAR Framework, the Canadian home care landscape has changed substantially, with the COVID-19 pandemic impacting service volumes, the types of services required; and health human resources, making workforce optimization using OCAR a high priority. The aim of this project was to assess the ongoing relevance and current use of the OCAR framework and identify opportunities for enhanced use both within and outside early adopter organizations. Methods: A cross-sectional approach was taken, using a web-based self-report survey of point-of-care leadership, clinical management, and practice and operations support (education, training, advance practice leaders) staff at SE Health. A proportional quota sampling strategy was employed, recruiting ~30% of eligible staff from each perspective. Participants were asked about the relevance of the OCAR Framework for daily practice; how frequently they used it for various care tasks (e.g., communicating client care needs); and to identify and rate importance of potential opportunities to improve its use (e.g., knowledge about PS provider roles) and helpful integration resources. Results: Survey respondents (n=99) overwhelmingly (81-95%) felt the OCAR Framework was often or almost always relevant to daily practice in the PS provider program. Notable findings include 81% of point-of-care staff report using OCAR regularly with client care documentation; 67% of clinical management often or almost always use OCAR when developing care plans; and 47% of practice and operation support staff often or almost always use OCAR when orienting new hires. Participants felt opportunities to improve SE Health’s use of OCAR were important to support consistency in client care across providers, improve role clarity (i.e., with both clients and staff), and ensure relevant, actionable information is available for providers. Learnings: Without regulation guiding practice standards, organizations are left to set education, training, and documentation requirements, in addition to outlining role and task descriptions. Variability in these professional practice requirements can create role confusion and barriers to integrated care. The OCAR Framework is a practical, relevant, and frequently used integration resource for home care providers, managers, and support staff within this homecare organization. Future planned work aims to expand the use of the OCAR Framework in Canada and beyond by co-designing an implementation toolkit to support delivery of integrated care by leveraging clinical contributions, improving communication, and integrating this important workforce into the home care team.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".