The influence of the learning climate on learning outcomes from Marte Meo counselling in dementia care
Bibliographic record
Abstract
AIM: To identify factors that affected the learning outcomes from Marte Meo counselling (MMC). BACKGROUND: Although MMC has shown promising results regarding learning outcomes for staff working in dementia-specific care units, the outcomes differ. METHOD: Twelve individual interviews and four focus group interviews with staff who had participated in MMC were analysed through a qualitative content analysis. RESULTS: The learning climate has considerable significance for the experienced benefit of MMC and indicate that this learning climate depends on three conditions: establishing a common understanding of the content and form of MMC, ensuring staff's willingness to participate and the opportunity to do so, and securing an arena in the unit for discussion and interactions. CONCLUSION: Learning outcomes from MMC in dementia-specific care units appear to depend on the learning climate in the unit. Implication for nursing management The learning climate needs attention from the nursing management when establishing Marte Meo intervention in nursing homes. The learning climate can be facilitated through building common understandings in the units regarding why and how this intervention should take place, and by ensuring clarity in the relationship between the intervention and the organization's objectives.
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 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 | 0.011 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".