Predictors of employment attrition in Lebanon during multifaceted crises: The role of chronic diseases – a national cross-sectional study
Bibliographic record
Abstract
Abstract The COVID-19 pandemic and Lebanon’s ongoing economic crisis exacerbated existing inequalities, including workforce disparities. This study identified predictors of employment attrition during Lebanon’s concurrent crises and examined the association between chronic conditions and employment attrition. This cross-sectional study recruited adults aged 19-64 years residing in Lebanon through random digit dialing (5 January – 9 July 2024). Data collected included socio-demographics, household characteristics, employment, and self-reported chronic conditions. The outcome was the loss of paid employment (employment attrition) during the crises. Predictors were identified through LASSO regression and model discrimination and calibration were assessed. Logistic regression models, adjusted for covariates identified through directed acyclic graphs, assessed the association between number and types of chronic conditions and employment attrition. Of 2103 participants employed prior to the onset of the concurrent crises (pre-2020), 72.7% were males, 70.1% were Lebanese, and 14.7% became unemployed during the crises. Predictors of employment attrition were: older age, females, non-Lebanese, married, no formal education, having at least one chronic condition, working in a private or non-governmental organization, and having an oral agreement with employer. The predictive model demonstrated a moderate to good discriminative ability and good calibration. Pre-existing chronic conditions, such as cardiovascular disease (aOR: 2.15; 95% CI, 1.27 to 3.64) and diabetes (aOR: 2.52; 95% CI, 1.43 to 4.45), were independently associated with employment attrition. This study underscores the need to address life-course disparities contributing to job loss and to consider proactive job protections to mitigate workforce disruptions during multiple crises, particularly in contexts where social safety nets are absent.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".