Evaluation of an Online Home Caregiver Support Program for Non-Professional Caregivers of Patients With Palliative Care Needs
Bibliographic record
Abstract
BACKGROUND: Caring for loved ones with palliative needs can be very stressful for carers'. To address this growing issue, an online Home Caregiver Support Program course was created to provide information to non-professional home caregivers about end-of-life care. OBJECTIVES: To measure non-professional caregivers' perceived level of competence in addressing physical, psychological, social, and spiritual needs before and after completing online training modules. METHODS: Learners rated their competence before and after completing online modules addressing 4 key dimensions relevant to palliative caregivers. Self-ratings of competence were assessed through surveys, completed before and after the online modules. Scores from before and after each module were compared to determine if the online course had increased participant competence. The Wilcoxon signed rank test was used to analyze participant responses to the pre- and post-survey questions. RESULTS: A total of 176 participants who completed one or more of the online modules between July 2017-December 2018, 70 (40%) of the participants completed at least one pre- and post-module survey and did not declare themselves as a professional caregiver. Participating in the online Home Caregiver Support Program increased participants' ratings of perceived competence in all domains (p < .01). This significance was maintained when professional caregivers were added to our analysis. CONCLUSION: After the completing the modules, participants' self-ratings of perceived competence increased suggesting that participants completing the online program had improved knowledge in addressing the physical, psychological, social, and spiritual challenges faced by non-professional caregivers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".