Visualizing participant experiences in maternal and child nutrition studies using timeline mapping
Bibliographic record
Abstract
Iron and folic acid (IFA) supplementation is one of the most cost-effective interventions to prevent and treat anemia during pregnancy. Despite having the highest global burden of anemia among pregnant women, rates of IFA uptake in pregnancy in India are still very low, particularly in the state of Uttar Pradesh. Timeline maps were developed as a visual qualitative tool to explore the nuances of health behaviors among pregnant women with respect to antenatal care (ANC) services, including IFA consumption. Timeline maps were used to elicit and visually document critical events pertaining to ANC services chronologically, including details on contact points with the health system and events specific to IFA distribution, consumption and counselling. The tool consists of a horizontal straight line with nine suspended boxes corresponding to each month of pregnancy, with legends on how to illustrate IFA receipt and consumption. In this instance, the woman's last menstrual period and expected date of delivery were used as a frame of reference for the duration of pregnancy. Six research assistants (RAs) were trained on how to use timeline maps to elicit and record participant narratives. The RAs later participated in a focus group discussion to gain insight about their experiences using the tool. The timeline maps were easy-to-use and facilitated in-depth conversations with participants. RAs were able to actively engage the participants in co-creating the maps. The visual nature of the tool prompted participants' recall of key pregnancy events and reflexivity. Challenges reported with the tool/process included recollection of past events and potential misrepresentation of information. These highlight a need to restructure training processes. Our findings indicate that timeline maps have the potential to be used in a variety of other program contexts, and merit further exploration.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.024 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.000 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".