Resilience for working in Ontario home and community care: registered practical nurses need the support of themselves, family and clients, and employers
Bibliographic record
Abstract
BACKGROUND: The context of practice is often not explicit in the discourse around the personal and professional resilience of nurses. The unique factors related to providing nursing care in home and community care may provide novel insight into the resilience of this health workforce. Therefore, this research addressed how nurses build and maintain resilience working in the home and community care sector. METHOD: A qualitative study was conducted between November 2022 to August 2023 using 36 in-depth interviews (29 registered practical nurses [RPNs], five supervisors of RPNs, two family/care partners (FCPs) of clients receiving home and community care services). Analysis was consistent with a grounded theory approach including coding and comparative methods. RESULTS: The factors of personal and professional resilience were not distinct but rather mixed together in the experience of nurses having resilience working in the home and community care sector. The process of building and maintaining resilience as home and community care nurses was informed by three categories: (1) The conditions of working in HCC; (2) The rapport RPNs held with FCPs; and (3) The nurses' ability for supporting the 'self'. Multiple components to inform these categories were identified and illustrated by the words of the nurse participants. CONCLUSION: The process of building and maintaining resilience by RPNs working in the home and community care sector was guided by the day-to-day experiences of providing care for clients and the conditions of being a mobile health care provider. However, nurses may sense when they need to support their 'self' and must be empowered to request and receive support to do so.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".