Feasibility of a microlearning intervention about nutritional care for older adults provided by hospital and home care nurses and nursing assistants: A mixed-methods study
Bibliographic record
Abstract
Background and objective: Hospital and home care nurses and nursing assistants do not provide optimal nutritional care to older adults, which is due to several factors that influence their current behaviour. To successfully target these factors, we developed a microlearning intervention. The next step is to assess its feasibility to achieve the best fit with nursing practice. The aim of this study was to test the feasibility of the microlearning intervention about nutritional care for older adults provided by hospital and home care nurses and nursing assistants.Methods: In a multicentre study, we used a mixed-methods design. Feasibility was determined by assessing 1) recruitment and retention of the participants and 2) the acceptability, compliance and delivery of the intervention. Data about the use of the intervention (consisting of 30 statements), and data from a standardised questionnaire and two focus group interviews were used to measure the feasibility outcomes.Results: Fourteen teams with a total of 306 participants (response rate: 89.7%) completed the intervention and the median (Q1, Q3) score for completed statements per participant was 23 (12, 28). The mean proportion of correct answers was 72.2%. Participants were both positive and constructive about the intervention. They confirmed that they mostly learned from the intervention. Overall, the intervention was acceptable to the participants and compliance and delivery was adequate.Conclusions: The microlearning intervention is mostly feasible for hospital and home care nurses and nursing assistants. Based on participants’ constructive feedback, we consider that the intervention needs refinement to improve its feasibility.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".