Enhancing health workforce capacity through life skills-based interventions: evidence from Pakistan on reducing gender-based violence
Bibliographic record
Abstract
BACKGROUND: Gender-based violence is a pervasive issue in Pakistan, necessitating effective interventions to enhance the responsiveness of healthcare workers' capacity to respond effectively to Gender-based violence. This study aims to strengthen the health system's response to Gender-based violence by implementing a need-based Life Skills-Based Training program for healthcare providers in Pakistan. METHOD: A quasi-experimental pre-post study design was employed. The intervention, i.e., Life Skills-Based Training program was implemented across four diverse districts Matiari and Shadadkot (Sindh), Chitral (Khyber Pakhtunkhwa), and Gilgit (Gilgit-Baltistan) to ensure geographic and sociocultural variation. A total of 84 healthcare providers and 78 stakeholders were purposively selected based on their roles in patient care or Gender-based violence-related policymaking. Life Skills-Based Training was delivered over six days at each site, including tailored modules for healthcare providers and stakeholders, focusing on Gender-based violence concepts, screening, counseling, referral pathways, and system-level advocacy. A pre- and post-test assessment was administered to measure changes in knowledge, competencies, and attitudes using structured questionnaires adapted from UNHCR and UNICEF tools. Quantitative analysis of score improvements and qualitative feedback were used to evaluate training effectiveness. RESULTS: A comparative analysis of pre- and post-test assessments demonstrated significant improvements in participants' Gender-based violence knowledge, awareness, and case management skills, with high satisfaction reported in Life Skills-Based Training evaluations. Feedback highlighted themes such as the effectiveness of training, the need for competent professionals, involving community leaders, replicating sessions, government health system responses, and integrating Gender-based violence education into curricula. Follow-up results demonstrated the sustainability of interventions, with participants actively applying their knowledge and leading community education. CONCLUSION: The study highlights the need for continuous capacity-building and integrating Gender-based violence education into healthcare and educational systems to improve support for survivors. Strengthening the health workforce with targeted training and protocols is essential for addressing Gender-based violence effectively, offering a framework for similar efforts in other regions to foster equitable and responsive healthcare.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".