<p>“When you least expect, this happens, it’s already growing”: Problematizing the definition of unmet need for family planning</p>
Bibliographic record
Abstract
Background: Unmet need is an important indicator to understand baselines and set goals for family planning interventions. Women may not fall neatly in categories of met or unmet need for family planning as defined by the demographic and health surveys (DHS). We explore women’s experiences of unmet need for family planning and provide empirical examples of how the static, binary DHS definitions of met and unmet need for family planning may be problematic. Methods: Based on Social Cognitive Theory, we conducted elicitation interviews with 16 married young women between the ages of 20 and 24 in Chimaltenango, Guatemala to explore barriers to accessing and using family planning. Half the participants (n=8) were using a modern method of family planning and half (n=8) were not. The current analysis focuses on data that was coded as ambiguous or unclear for unmet need status. Results: We identified framings of ambiguity from the women’s narratives that are silenced by the dominant binary of met and unmet need. We show inconsistencies between women’s lived experiences of unmet need and how their experiences would likely be represented in DHS questionnaires: 1) successful use of natural methods; 2) the complexity of “wantedness”; 3) conceptualizing met or unmet need as a trajectory; and 4) laughter obscuring clear response. Conclusion: Family planning status is a complex trajectory that the DHS may not accurately capture. As a way to reflect the diversity of women’s family planning experiences, we suggest modifying the DHS classifications to incorporate latent met and unmet need as sub-classifications. Keywords: contraception, pregnancy intention, qualitative research methods, Latin America and the Caribbean
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".