Barriers and Facilitators to Implementing Web-Based Dementia Caregiver Education From the Clinician’s Perspective: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Internet-based dementia caregiver interventions have been shown to be effective for a range of caregiver outcomes; however, little is known about how to best implement them. We developed iGeriCare, an evidence-based, multimedia, web-based educational resource for family caregivers of people living with dementia. OBJECTIVE: This study aims to obtain feedback and opinions from experts and clinicians involved in dementia care and caregiver education about 1 iGeriCare and 2 barriers and facilitators to implementing a web-based caregiver program. METHODS: We carried out semistructured interviews with individuals who had a role in dementia care and/or caregiver education in several key stakeholder settings in Southern Ontario, Canada. We queried participants' perceptions of iGeriCare, caregiver education, the implementation process, and their experience with facilitators and barriers. Transcripts were coded and analyzed using a grounded theory approach. The themes that emerged were organized using the Consolidated Framework for Implementation Research. RESULTS: A total of 12 participants from a range of disciplines described their perceptions of iGeriCare and identified barriers and facilitators to the implementation of the intervention. The intervention was generally perceived as a high-quality resource for caregiver education and support, with many stakeholders highlighting the relative advantage of a web-based format. The intervention was seen to meet dementia caregiver needs, partially because of its flexibility, accessibility, and compatibility within existing clinical workflows. In addition, the intervention helps to overcome time constraints for both caregivers and clinicians. CONCLUSIONS: Study findings indicate a generally positive response to the use of internet-based interventions for dementia caregiver education. Results suggest that iGeriCare may be a useful clinical resource to complement traditional face-to-face and print material-based caregiver education. More comprehensive studies are required to identify the effectiveness and longevity of web-based caregiver education interventions and to better understand barriers and facilitators with regard to the implementation of technology-enhanced caregiver educational interventions in various health care settings.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".