The problem of social desirability bias when measuring desire for adolescent pregnancy
Bibliographic record
Abstract
Accurate reporting of pregnancy desire is instrumental to develop programmes that meet the needs of adolescents and can ensure their right to safety and support during their development into adulthood. In the paper by Estrada et al. (BJOG 2018;125:1330–6), the authors present more needed data on pregnancy desire in Latin America through secondary analysis of the UNICEF Multiple Indicator Cluster Surveys (https://mics.unicef.org/surveys). In this study, the authors found that pregnancy desire varied significantly by region, ranging from 38% in Panama to 79% in Cuba. The authors use robust methodology to account for between-country differences and identified that older adolescents with lower levels of education and lower parity and who were already married or cohabiting were more likely to desire their pregnancy. These findings have significant implications for effective programme design to reduce the adolescent pregnancy rate. In this analysis, the authors rightly state a limitation of their data as ‘a proportion of the population was retrospectively reporting pregnancy desire and would be subject to recall bias while others were currently pregnant and biased by social desirability’. The issue of social desirability bias is one that probably affects the entire population included in this study. It may have led to inflated estimates of pregnancy desire in some respondents, particularly those who are older and cohabiting, and reduced reporting in those who are younger or more educated, as is represented in the findings of this study. Social desirability bias is an issue that could and should be addressed in future research on pregnancy desire in adolescents. It is motivated by psychosocial determinants, such as the need for social approval and avoidance of embarrassment, and can also be determined by survey type, location and time (Krumpal Qual Quant 2013;47:2025–47). Methods to address social desirability bias in sexual health surveys have been presented, including indirect questioning and informal confidential voting interviews (Gregson et al. Sexually Transm Dis 2002;29:568–75). Reducing adolescent pregnancy is a health and human rights issue that requires urgent attention if we are to meet our global targets for health and prosperity set out in the Sustainable Development Goals. Specifically, programmes are needed that support the reduction of adolescent pregnancy in a respectful and inclusive manner, and that promote responsible and healthy reproductive and sexual behaviours, to ensure that girls meet their full potential. Effective programme design requires understanding the needs of adolescent girls, including the factors motivating their desire to become pregnant at such a young age. Estrada et al. begin to fill the gap in knowledge regarding pregnancy desire in Latin American adolescents, but further work is required that takes into account the social structure, societal norms, and individual and collective values surrounding girls during their adolescent years. The author would like to acknowledge thanks to Sumedha Sharma and Marianne Vidler for their editorial assistance. None declared. Completed disclosure of interests form available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.413 | 0.648 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".