Designing key performance indicators (KPIs) for decent work in the pharmaceutical supply chain of Iran
Bibliographic record
Abstract
While decent work has emerged as the central theme of the psychology of work theory and a global concept and directive for promoting social, political, and economic justice, it has garnered increasing scientific and political attention in the past two decades. However, until now, no defined measurement scale for the pharmaceutical supply chain exists. The present study aims to design and validate key performance indicators (KPIs) for 'decent work' in the pharmaceutical industry supply chain of Iran, using the Decent Work Daffi Scale (2017) as a reliable framework with five sub-scales and 15 items, tested and validated. For the validation of the Decent Work Scale, a quantitative survey study was conducted among selected pharmaceutical industry experts with a sample size of 228 individuals in the year 2023. The current study adopted an exploratory factor analysis approach using SPSS software and a confirmatory factor analysis through AMOS version 24 software. In this context, the factor structure, convergent validity, discriminant validity, and Cronbach's alpha coefficients were examined. The results showed that the five-factor structure outperforms the one-factor model with evidence supporting the convergent, discriminant, and predictive validity of the five-factor scale. Thus, the measurement of decent work in the pharmaceutical industry of Iran comprises five sub-scales: occupational safety conditions, access to healthcare, adequate remuneration, Free time and rest, and alignment of organizational values with family and societal values. This scale can serve as a useful tool for industrial and organizational psychology research, as well as for studies on the sustainability of social supply chains.
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How this classification was reachedexpand
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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".