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Record W2046336010 · doi:10.1371/journal.pone.0110940

The Orthopaedic Trauma Patient Experience: A Qualitative Case Study of Orthopaedic Trauma Patients in Uganda

2014· article· en· W2046336010 on OpenAlexaff
Nathan N. O’Hara, Rodney Mugarura, Gerard P. Slobogean, Maryse Bouchard

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineReferralThematic analysisSocioeconomic statusQualitative researchHealth careFamily medicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

The disability adjusted life years (DALYs) associated with injuries have increased by 34% from 1990 to 2010, making it the 10th leading cause of disability worldwide, with most of the burden affecting low-income countries. Although disability from injuries is often preventable, limited access to essential surgical services contributes to these increasing DALY rates. Similar to many other low- and middle-income countries (LMIC), Uganda is plagued by a growing volume of traumatic injuries. The aim of this study is to explore the orthopaedic trauma patient's experience in accessing medical care in Uganda and what affects the injury might have on the socioeconomic status for the patient and their dependents. We also evaluate the factors that impact an individual's ability to access an appropriate treatment facility for their traumatic injury. Semi-structured interviews were conducted with patients 18 year of age or older admitted with a fractured tibia or femur at Mulago National Referral Hospital in Kampala, Uganda. As limited literature exists on the socioeconomic impacts of disability from trauma, we designed a descriptive qualitative case study, using thematic analysis, to extract unique information for which little has been previously been documented. This methodology is subject to less bias than other qualitative methods as it imposes fewer preconceptions. Data analysis of the patient interviews (n = 35) produced over one hundred codes, nine sub-themes and three overarching themes. The three overarching categories revealed by the data were: 1) the importance of social supports; 2) the impact of and on economic resources; and 3) navigating the healthcare system. Limited resources to fund the treatment of orthopaedic trauma patients in Uganda leads to reliance of patients on their friends, family, and hospital connections, and a tremendous economic burden that falls on the patient and their dependents.

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)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0150.009
Scholarly communication0.0050.005
Open science0.0030.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.323
Teacher spread0.259 · 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 designQualitative
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

Citations42
Published2014
Admission routes1
Has abstractyes

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