Hospital Paperworlds: Medical (Mis)Reporting and Maternal Health in Northern Pakistan
Bibliographic record
Abstract
Global health metrics come into being in complex circumstances. Through ethnography that focuses closely on the forces driving uneven obstetric case reporting in a government hospital in northern Pakistan, this article challenges the integrity of the health care system documentation on which the state and non-state interventions and evaluations rely. Incomplete and skipped case records not only resulted from the time constraints posed by work on a busy maternity ward. They also helped vulnerable frontline providers disguise and avoid accountability for the aftermaths of the medical mismanagement and maltreatment made more likely by infrastructural scarcity and disarray. Yet the provider-side protections these tactics afforded came at patients' expense because they rendered error, wrongdoing, and iatrogenesis as invisible and unactionable. The sum of these reporting practices was "hospital paperworlds": defensively authored and aspirational datasets that conveyed desired rather than achieved outcomes, decontextualized risks and harms, and were too-rarely triangulated for their correlational significances or deficiencies. [hospital ethnography, obstetrics, case reporting, metrics, Pakistan].
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".