Social Support and Types of Treatment for Illness in Fonfrede Haiti
Bibliographic record
Abstract
The purpose of this study was to examine the relationship between sources of social support and subjects’ preferred treatment of illness. After the 2010 earthquake in Haiti, health care workers and facilities were scarce; 10 percent of Haiti's health care staff was killed and 60 percent of the health care facilities destroyed. The study was based on social support as a psychosocial factor serving as an intermediary between environmental stress and an individual's preference for treatment of their illness. The literature suggests that social support improves treatment of illness and health outcomes through stress buffering and main effects. This study of 110 Haitians was conducted two years after the earthquake in a small town outside Port au Prince. Participants mean age was 39.85 years; 57% were female. The instrument used was designed to determine the sources of social support (family, friends, church, community) and how illnesses were managed (“ medsin ” prepared by an herbalist and often related to voodoo, tonics or teas, vitamins and prescription drugs). A backwards stepwise logistic regression was conducted for each of the variables. If the church was used for social support, prescription drugs were primarily used (r = 0.20; p = 0.0261); if friends provided illness care, vitamins were used (r = 0.20; p = 0.0263); if community organizations used, vitamins were used (r = 0.20; p = 0.027) and when family provided care, medsin and teas were used (r = 0.20; p = 0.0268). Participants relied on a variety of sources for illness and included prescription medicines, herbal remedies and a Haitian tea . Participants indicated a high importance placed on the church as a source of social support. Reliance on the church is understandable based on the culture and often provides comfort.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".