Are we equipped with serving the right care? Implications for program responses regarding gender-based violence
Bibliographic record
Abstract
Abstract Background Globally, 35% of women have experienced gender-based violence (GBV) which seriously affects all aspects of women’s health. While health sector must play a key role in response, there are many barriers for GBV survivors to access health services, especially in developing countries including Myanmar. Limitations of health sector in provision of quality services to GBV survivors, healthcare providers’ knowledge, attitude, experience and service availability and readiness, should be explored as an initial step for the improvement of health care response to GBV survivors.Methods This study was a cross-sectional descriptive study conducted in four purposively selected townships with higher number GBV cases. Face-to-face interviews were done to all health care providers (n=233) from public health facilities using a structured questionnaire. The findings were described as frequency and percentage for categorical data and mean and standard deviation for continuous data.Results Lady Health Visitors and Midwives were mainly involved (88.0%). About two-thirds had heard GBV without probing. Types of violence they mostly described were physical (81.1%) and sexual violence (8.5%). One-third wanted women to be patient to their partners’ violence to maintain family ties. Nearly two-third assumed conflict between husband and wife was not a matter that someone should involve. About 70% had given care to GBV survivors and they provided only injury treatment (76.1%). A quarter of them experienced sexual violence cases, but only 4.9% and 1.2% provided emergency contraception and Sexually Transmitted Infection treatment. Although nearly two third mentioned about psychological counseling in GBV management, only 20% provided counseling services to survivors. Absence of standard GBV management guideline, trained and skilled staff for GBV and counseling room at health facilities were issues mostly stated by the respondents.Conclusions Inadequate knowledge, misconceptions and unfavorable attitudes of GBV among health care providers might deter the effectiveness of GBV management at the health sector. In addition, poor management practice together with no standard management guideline, limited skilled staff, inadequate drug supplies and absence of counseling facilities indicated insufficient readiness to provide quality health care responses to GBV surviours in Myanmar.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".