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Record W2035205140 · doi:10.5539/gjhs.v4n6p47

Clinical Complaints amongst Patients in a Guyanese Prison

2012· article· en· W2035205140 on OpenAlexaffvenueabout
Raywat Deonandan, Jessica Wynn Lockhart, Brenna Mahoney, Glenda Mindlin, Joanne Laine-Gossin, Nazmoon Audam, Louis H. Nel, Melissa Sissons, Bekkie Vineberg

Bibliographic record

VenueGlobal Journal of Health Science · 2012
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPrisonPsychologyMedicineCriminology

Abstract

fetched live from OpenAlex

BACKGROUND: Incarcerated populations are at particular risk for developing specific health conditions. Prior studies of prisons in developing countries have focused on the threat of communicable diseases, though anecdotal evidence suggests that chronic conditions are of particular concern. This study constitutes the first published investigation of health complaints offered by residents of a prison in the South American nation of Guyana. METHOD: In 2010, a medical team sent by the Toronto non-governmental organization Ve'ahavta visited the Mazaruni prison in the interior of Guyana. Data on patient encounters was collected as part of the triage activity. RESULTS: Care was given to 108 patients, staff and family members. Contrary to literature expectations, 50% of complaints concerned musculoskeletal issues, while only 11% were genitor-reproductive. Upon examination, 30.6% of patients were experiencing musculoskeletal problems, most commonly back pain. CONCLUSION: Future medical interventions to this and comparable low- and middle-income country prisons should more vigorously consider physiotherapeutic interventions, in addition to the expected addressing of infectious diseases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.127
GPT teacher head0.545
Teacher spread0.418 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2012
Admission routes3
Has abstractyes

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