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Record W2560608767 · doi:10.2106/jbjs.15.01299

Orthopaedic Trauma Care Capacity Assessment and Strategic Planning in Ghana: Mapping a Way Forward

2016· article· en· W2560608767 on OpenAlexaff
Barclay T. Stewart, Adam Gyedu, Gavin Tansley, Dominic Yeboah, Forster Amponsah‐Manu, Charles Mock, Wilfred Labi-Addo, Robert Quansah

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsDalhousie University
FundersFogarty International CenterNational Institutes of Health
KeywordsMedicineHealth carePopulationOrthopedic surgeryMedical emergencyOperations managementEnvironmental healthEngineeringSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Orthopaedic conditions incur more than 52 million disability-adjusted life years annually worldwide. This burden disproportionately affects low and middle-income countries, which are least equipped to provide orthopaedic care. We aimed to assess orthopaedic capacity in Ghana, describe spatial access to orthopaedic care, and identify hospitals that would most improve access to care if their capacity was improved. METHODS: Seventeen perioperative and orthopaedic trauma care-related items were selected from the World Health Organization's Guidelines for Essential Trauma Care. Direct inspection and structured interviews with hospital staff were used to assess resource availability and factors contributing to deficiencies at 40 purposively sampled facilities. Cost-distance analyses described population-level spatial access to orthopaedic trauma care. Facilities for targeted capability improvement were identified through location-allocation modeling. RESULTS: Orthopaedic trauma care assessment demonstrated marked deficiencies. Some deficient resources were low cost (e.g., spinal immobilization, closed reduction capabilities, and prosthetics for amputees). Resource nonavailability resulted from several contributing factors (e.g., absence of equipment, technology breakage, lack of training). Implants were commonly prohibitively expensive. Building basic orthopaedic care capacity at 15 hospitals without such capacity would improve spatial access to basic care from 74.9% to 83.0% of the population (uncertainty interval [UI] of 81.2% to 83.6%), providing access for an additional 2,169,714 Ghanaians. CONCLUSIONS: The availability of several low-cost resources could be better supplied by improvements in organization and training for orthopaedic trauma care. There is a critical need to advocate and provide funding for orthopaedic resources. These initiatives might be particularly effective if aimed at hospitals that could provide care to a large proportion of the population.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.067
GPT teacher head0.305
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2016
Admission routes1
Has abstractyes

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