Effect of Anemia on Work Productivity in Both Labor- and Nonlabor-Intensive Occupations: A Systematic Narrative Synthesis
Bibliographic record
Abstract
BACKGROUND: Anemia is highly prevalent particularly in low- and middle-income countries. Iron deficiency contributes to an estimated 50% of anemia cases. Iron interventions have become central to global anemia treatment and prevention; however, few iron interventions have been scaled up to the national level, despite their proven effectiveness. While both cross-sectional and interventional studies on the effect of anemia and iron supplementation on worker productivity have been conducted, there have been few systematic reviews conducted. As such, a synthesis of previously conducted primary research is warranted and may provide a more comprehensive overview of the strength of currently available evidence, potentially helping to inform national policy on matters relating to funding and legislation for population-level iron interventions. OBJECTIVES: The objective of this study was to synthesize available evidence on the effect of both anemia and therapeutic iron interventions on productivity in working adults. METHODS: All relevant English language studies were systematically obtained from both MEDLINE and EMBASE and assessed for evidence of differing productivity levels across groups stratified by iron intervention or anemia status. Potential mediating variables were reported, and the results were narratively summarized. RESULTS: The available data from 12 included studies provide strong evidence that anemia negatively impacts occupational performance and that therapeutic iron interventions can yield substantial productivity gains. CONCLUSIONS: Despite their limitations, these findings make an important contribution to the literature highlighting the impact of iron deficiency and population-wide iron interventions on work productivity and occupational performance.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".