Abstract W P273: Health Care Professionals’ Perspectives on Implementing Family Caregiver Education and Support Programs into the Ontario Stroke System
Bibliographic record
Abstract
Introduction: Family caregivers play a key role in the care of stroke survivors post-discharge. Without a standard of practice for supporting caregivers, many experience negative health outcomes. Literature supports establishing system-level infrastructure to provide education and support to family caregivers. However, the implementation of such programs has yet to be actualized. Objective: This study examined factors that influence the implementation of caregiver programs into the Ontario Stroke System from the perspectives of healthcare professionals Methods: Health care professionals providing stroke care in acute, rehabilitation and community care settings were invited to participate in the study by their Regional Education Coordinators. Health care professionals who were interested in participating in the study contacted the first author to set up the interview. Semi-structured interviews were conducted with participants. Interviews were audiotaped, professionally transcribed and reviewed for accuracy. Transcripts were coded, data was analyzed using a constant comparison approach and themes were identified. Results: A total of nineteen health care professionals participated in this study (7 acute, 5 rehab, 6 community, 1 private). Interviews lasted between 28 and 74 minutes. Data analysis identified five themes: 1) It is important for key stakeholders to understand the value of caregiver education and support programs; 2) Caregiver education and support requires dedicated resources; 3) Delineate ownership and responsibility for program implementation; 4) Provide training to health care professionals regarding caregiver needs and program materials to obtain buy-in and facilitate implementation; and 5) Establish a clear identity for the program and promote awareness of the program to potential implementers and users. Conclusions: This is the first study to identify factors that influence the implementation of family caregiver education and support programs from the perspectives of health care professionals involved in providing stroke care. Addressing these factors will enable the health care system to establish such programs, ensuring family caregivers receive the support they need.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".