Examining depression and quality of life in patients with Thalassemia in Sri Lanka
Bibliographic record
Abstract
Background: International healthcare initiatives must evolve from individual programs that target single diseases to systems of care that address the needs of the whole person.Since 2001, the World Health Student Organization (WHSO) at Wayne State University School of Medicine has sent medical students and physicians to Central and South America on medical relief trips.WHSO annually sends three teams to Morne, L'Hopital, Haiti to provide sustainable healthcare and facilitate continuity of care.A needs assessment was developed to evaluate the community's healthcare needs.Methods: Needs assessments may be structured as focus groups, traditional surveys, or both.Questions generally diverge into three categories: demographics, healthcare access, and healthcare delivery.Considering the short-term nature of our relief trips, a 15 minute assessment was developed that focused on the above three themes.Participants were chosen randomly from a triage waiting area, consented, and then completed the survey via Creole interpreters.34 surveys were completed by men and women age 18 and older.This study was approved by the Haitian Ethics Committee and the Wayne State University Institutional Review Board.Findings: Results include age, marital status, years of education, household size, number of children, availability of running water and electricity, occupation, income, perceived health problems, methods of accessing their healthcare systems, and barriers to obtaining needed care.The most common perceived problems were gastrointestinal disease, headache, fever, abdominal pain, and anemia.Respondents thought a nearby clinic, clean water, more medications, more money, and electricity would be most beneficial for their health.Barriers to healthcare access included lack of monetary funds and long travel times.Interpretation: International relief work struggles to provide efficacious care to lower-and-middle income countries, which may be due to a lack of understanding of cultural and socioeconomic factors that affect healthcare delivery.The needs assessment is the first step to identifying these key factors, in an effort to ultimately deliver community requested healthcare in a culturally competent manner.WHSO aims to avoid "creating a problem then prescribing a solution", and instead partner with the community and Haitian organizations to provide healthcare the community values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".