How High-Performing Personal Support Workers Set and Maintain Boundaries When Providing Care: A Case Study in Ontario, Canada
Bibliographic record
Abstract
Personal support workers (PSWs) provide a large proportion of in-home care services for Canadians. PSWs must negotiate with clients and their family on how prescribed care is delivered. How PSWs set and maintain professional and personal boundaries during care is poorly understood, and failure to manage boundaries can expose both PSWs and clients to risk. High-performing PSWs ( n = 9) and supervisors ( n = 4) within an Ontario, Canada, home care agency were engaged in workshops ( n = 3) to identify field-tested strategies and tactics for identifying, managing, and supporting PSW boundaries. A boundary-management framework was generated, including types of boundary challenges; decision-making principles (e.g., consider the purpose of home care); response strategies (e.g., work with the client on an alternative solution); and tactics for action (e.g., use proactive reminders). Supervisory and organizational supports (e.g., enabling “shop-talk”) were identified. The framework can inform teaching and practice materials for PSWs in Canada and other countries.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".