Perpetuation of gender discrimination in Pakistani society: results from a scoping review and qualitative study conducted in three provinces of Pakistan
Bibliographic record
Abstract
BACKGROUND: Gender discrimination is any unequal treatment of a person based on their sex. Women and girls are most likely to experience the negative impact of gender discrimination. The aim of this study is to assess the factors that influence gender discrimination in Pakistan, and its impact on women's life. METHODS: A mixed method approach was used in the study in which a systematic review was done in phase one to explore the themes on gender discrimination, and qualitative interviews were conducted in phase two to explore the perception of people regarding gender discrimination. The qualitative interviews (in-depth interviews and focus group discussions) were conducted from married men and women, adolescent boys and girls, Healthcare Professionals (HCPs), Lady Health Visitors (LHVs) and Community Midwives (CMWs). The qualitative interviews were analyzed both manually and electronically through QSR NVivo 10. The triangulation of data from the systematic review and qualitative interviews were done to explore the gender discrimination related issues in Pakistan. RESULTS: The six major themes have emerged from the systematic review and qualitative interviews. It includes (1) Status of a woman in the society (2) Gender inequality in health (3) Gender inequality in education (4) Gender inequality in employment (5) Gender biased social norms and cultural practices and (6) Micro and macro level recommendations. In addition, a woman is often viewed as a sexual object and dependent being who lacks self identity unless being married. Furthermore, women are restricted to household and child rearing responsibilities and are often neglected and forced to suppress self-expression. Likewise, men are viewed as dominant figures in lives of women who usually makes all family decisions. They are considered as financial providers and source of protection. Moreover, women face gender discrimination in many aspects of life including education and access to health care. CONCLUSION: Gender discrimination is deeply rooted in the Pakistani society. To prevent gender discrimination, the entire society, especially women should be educated and gendered sensitized to improve the status of women in Pakistan.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".