Constructing a questionnaire for assessment of awareness and acceptance of diversity in healthcare institutions
Bibliographic record
Abstract
BACKGROUND: To develop a healthcare environment that is congruent with diversity among care providers and care recipients and to eliminate ethnic discrimination, it's important to map out and assess caregivers' awareness and acceptance of diversity. Because of a lack of standardized questionnaires in the Swedish context, this study designed and standardized a questionnaire: the Assessment of Awareness and Acceptance of Diversity in Healthcare Institutions (AAAD, for short). METHOD: The questionnaire was developed in four phases: a comprehensive literature review, face and content validity, construct validity by factor analysis, and a reliability test by internal consistency and stability assessments. RESULTS: Results of different validity and reliability analyses suggest high face, content, and construct validity as well as good reliability in internal consistency (Cronbach's alpha: 0.68 to 0.8) and stability (test-retest: Spearman rank correlation coefficient: 0.60 to 0.76). The result of the factor analysis identified six dimensions in the questionnaire: 1) Attitude toward discrimination, 2) Interaction between staff, 3) Stereotypic attitude toward working with a person with a Swedish background, 4) Attitude toward working with a patient with a different background, 5) Attitude toward communication with persons with different backgrounds, 6) Attitude toward interaction between patients and staff. CONCLUSION: This study introduces a newly developed questionnaire with good reliability and validity values that can assess healthcare workers' awareness and acceptance of diversity in the healthcare environment and healthcare delivery.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".