The willingness of orthopaedic trauma patients in Uganda to accept financial loans following injury
Bibliographic record
Abstract
BACKGROUND: Early access to a monetary loan may mitigate some of the socioeconomic burden associated with surgical treatment and lost wages following injury. The primary objective of this study was to determine the willingness of orthopaedic trauma patients in Uganda to accept a formal financial loan shortly after their time of injury. METHODS: A consecutive sample of adult orthopaedic trauma patients admitted to Uganda's national referral hospital was included in the survey. The primary outcome was the self-reported willingness to accept a financial loan. Secondary outcomes included the preferred loan terms, fracture treatment costs, and the factors associated with loan willingness. RESULTS: Of the 40 respondents (mean age, 40 years; 58% male), the median annual income was $582 United States dollars (USD) (range: $0-$6720). Around 50% reported a willingness to accept a loan with any terms. Patients requested loans with a median principal of $500 USD and a median interest rate of 5% with 12 months to pay back. Patients had received loans with a median principal of $142 USD, an interest rate of 10%, and payback of 6 months. These received loans covered a mean of 63% of the treatment costs. Patients with higher median incomes ($857 USD vs $342 USD) were more willing to accept a loan. CONCLUSION: This study demonstrated a limited interest of orthopaedic trauma patients in Uganda to procure loans through formalized lending. This observed resistance must be overcome in future programs that rely on mechanisms such as conditional cash transfers or microfinancing to improve clinical and socioeconomic outcomes after injury.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".