Adherence to treatment and harmful effects of medication shortages in the context of severe crises: scale validation and correlates
Bibliographic record
Abstract
BACKGROUND: Medication shortage is a public health problem, affecting patients' outcomes mainly through the difficulty in maintaining adherence, particularly in the context of a severe economic crisis. There is a need for a new scale that assesses the effect of medication shortage on adherence. AIM: To develop and validate a scale to evaluate the harmful impact of medication shortage among the general Lebanese population and assess its correlates and association with medication adherence. METHODS: A questionnaire was used to assess medication shortage harmful effects and patients' adherence, allowing to generate the Harmful Impact of Medication Shortage scale (HIMS). The factor analysis, convergent validity and reliability of the generated scale were assessed, followed by multivariable regressions to evaluate its correlates. RESULTS: The developed HIMS scale is a 9-item tool, used to assess how difficult it was for people to deal with medication shortages and their harmful effects on treatment. It was significantly and inversely linked to treatment adherence and affected by the patients' socioeconomic status and the type of chronic disease. CONCLUSION: The Harmful Impact of Medication Shortage scale could be an efficient tool to measure the detrimental effects of medication shortages among the Lebanese adult population with chronic diseases, particularly affecting treatment adherence. Future studies and evidence are still needed to confirm our findings and help build global mitigation policies addressing medication shortages.
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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 | 0.001 | 0.003 |
| 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 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".