Expanding the enablement framework and testing an evaluative instrument for diabetes patient education
Bibliographic record
Abstract
BACKGROUND: Strategies to delay or prevent complications from diabetes include diabetes patient education. Diabetes educators seek to provide education that meets the needs of clients and influences positive health outcomes. AIMS: (1) To expand prior research exploring an enablement framework for patient education by examining perceptions of patient education by persons with diabetes and (2) to test the mastery of stress instrument (MSI) as a potential evaluative instrument for patient education. METHOD: Triangulated data collection with a convenience sample of adults taking diabetes education classes. Half the sample completed audio-taped semi-structured interviews pre, during and posteducation and all completed the MSI posteducation. Qualitative data were analysed using latent content analysis, descriptive statistics were completed. RESULTS: Qualitative analysis revealed content categories similar to previous work with prenatal participants, supporting the enablement framework. Statistical analyses noted congruence with psychometric findings from development of MSI; secondary qualitative analyses revealed congruency between MSI scores and patient perceptions. CONCLUSIONS: Mastery is an outcome congruent with the enablement framework for patient education across content areas. Mastery of stress instrument may be a instrument for identification of patients who are coping well with diabetes self-management, as well as those who are not and who require further nursing interventions.
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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 | 0.064 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".