Effect of Iron Deficiency on Worker Productivity through Production Function Intermediates
Bibliographic record
Abstract
The objective of this study was to determine the mechanisms of the effect of iron deficiency on worker productivity through production function intermediates. Female subjects (n=217, age=18–55 y) participated in a dietary iron intervention (double fortified salt, DFS) on a tea estate in West Bengal, India. Productivity was assessed as work output measured in kilograms of tea picked per day over one week. The intermediates were iron status, measured through total body iron; energy expenditure, estimated through accelerometry and heart rate monitoring; cognitive function, measured through the attention network task, simple reaction test, temporal threshold task, and contrast threshold task; infectious diseases morbidity, measured through white blood cell counts, alpha‐1‐acid glycoprotein, and C‐reactive protein; physiological health, measured through body mass index and mid‐upper arm circumference; and labor/leisure trade‐off, measured through changes in discretionary time outside of work. The difference‐in‐difference method was used to find the changes in productivity between the treatment (DFS) and control (iodine only) groups. The Oaxaca‐Blinder Decomposition was applied to decompose the total change in worker output into two parts: changes in each production function input and the changing returns of those inputs. Supported by the Mathile Institute and Micronutrient Initiative
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".