Psychometric validation of the VIP care battery and composite scale for vulnerable populations inclusiveness in pharmaceutical care
Bibliographic record
Abstract
Inclusiveness in healthcare encompasses inclusivity, diversity, and equity, and is hypothesized to be affected by healthcare professionals' ethical attitudes and moral behaviors, emotional intelligence, and well-being. This study aimed to validate a battery of tests and develop a composite scale to assess inclusiveness components in pharmaceutical care among a sample of community pharmacists. A self-administered questionnaire was distributed to community pharmacists and students training in community pharmacies. The questionnaire collected participants' sociodemographic characteristics and used validated scales to assess their inclusivity, prioritization of patients, ethical attitudes, moral behaviors, emotional intelligence, well-being, and work fatigue. After confirming scale validity, clustering analysis was conducted, a composite scale was derived (VIP-Care Scale), and correlates of inclusiveness scores were identified. The validated battery comprised three components: (1) Healthcare Equity, Inclusivity, and Professional Integrity; (2) Moral and Ethical Perceptions; and (3) Psychological Wellness and Interpersonal Skills. Overall, participants demonstrated moderate levels of inclusiveness. Female gender, working in high-volume pharmacies (> 100 patients/day), and extensive experience (> 12 years) were significantly associated with higher inclusiveness scores. Conversely, lower inclusiveness was linked to working 1-16 h per week and the absence of a designated place in the pharmacy for discussing confidential information. This study could validate a battery of tests and an associated composite scale (the VIP-Care Scale) to measure inclusiveness in pharmaceutical care and identify its correlates. The scales demonstrated robust construct and structural validity in exploratory and confirmatory factor analyses, with excellent internal consistency, establishing the battery's validity and reliability. Future validation should assess test-retest reliability, cross-cultural applicability, and convergent validity.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".