Advancing a relational approach through online education: A mixed methods assessment in older adults' care
Bibliographic record
Abstract
OBJECTIVE: To evaluate the experience of health-care workers who completed an online professional development program designed to support family carergivers in care teams in personal care homes. The evaluation focussed on issues of access and uptake of education modules. METHODS: An explanatory sequential mixed methods design was used to include data from posteducation surveys, and online module analytics of use, to inform interview questions. Demographic data, survey data and video analytics were described. Focus group data were analysed using reflexive thematic analysis. Mixed method data integration enabled inference development. RESULTS: Across eight personal care homes located in Canada and Australia, of 114 health-care workers (mainly care aides) who commenced the online education modules, 89 watched and completed the postassessment. Most agreed that the educational content increased their understanding, and the videos increased their knowledge. Ten participants took part in focus groups interviews. The education was perceived as another way to learn and was considered beneficial for individual and team practice. Health-care workers found it difficult to find time to undertake learning opportunities outside work when their lives were busy and involved other responsibilities or multiple jobs. The need for organisational support to coordinate time during work to undertake work-related learning was highlighted. CONCLUSIONS: Caregiver-centred care education exemplifies enhancing relational care among health-care staff and family caregivers, beneficial for individual and team practice. By valuing education for health-care workers through creating opportunities for staff to engage during work time, a cultural environment that embodies continuous learning can be created in sectors caring with older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".