Factors Associated With the Effectiveness of Continuing Education in Long-Term Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: This article examines factors within the long-term-care work environment that impact the effectiveness of continuing education. DESIGN AND METHODS: In Study 1, focus group interviews were conducted with staff and management from urban and rural long-term-care facilities in southwestern Ontario to identify their perceptions of the workplace factors that affect transfer of learning into practice. Thirty-five people were interviewed across six focus groups. In Study 2, a Delphi technique was used to refine our list of factors. Consensus was achieved in two survey rounds involving 30 and 27 participants, respectively. RESULTS: Management support was identified as the most important factor impacting the effectiveness of continuing education. Other factors included resources (staff, funding, space) and the need for ongoing expert support. IMPLICATIONS: Organizational support is necessary for continuing education programs to be effective and ongoing expert support is needed to enable and reinforce learning.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it