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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".