Structural violence and institutionalized individuals: A paleopathological perspective on a continuing issue
Bibliographic record
Abstract
Past and present institutions (e.g., state and public hospitals, assisted living facilities, public nursing homes) have struggled with structural issues tied to patient care and neglect, which often manifests in the form of fracture trauma, and may explain why institutionalized individuals are at higher risk for this injury. Six hundred individuals from the Robert J. Terry Anatomical Collection born between 1822-1877 were examined to investigate hip fracture prevalence. Analysis of associated records and documentary data, including death, morgue, and census records, revealed that 36.3% (n = 218) of these individuals died in institutions such as the St. Louis State Hospital, City Infirmary, and Missouri State Hospital No. 4. Of the institutionalized individuals, 4.3% had evidence of hip fracture, significantly higher than the non-institutionalized (2.3%). Records revealed that many hip fractures were suffered around the time of death in state hospitals and were preventable, resulting from structural issues tied to understaffing and underfunding. Forensic and clinical literature, as well as current news media, indicate that structural violence in the forms of underfunding and understaffing continues to manifest as hip fractures harming institutionalized individuals today. This paper demonstrates how an anthropological perspective using paleopathological analysis sheds light on the chronicity and time depth of this issue, with the aim of driving public policy to entrench the equitable care of institutionalized people as a human right.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.066 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".