Examining the Traits-Desires-Intentions-Behavior (TDIB) Model for Fertility Planning in Women Living with HIV in Ontario, Canada
Bibliographic record
Abstract
The objective of this study was to determine the predictors of fertility behavior (i.e., trying to become pregnant) in a large representative sample of women living with HIV of reproductive age in Ontario, Canada. The Traits-Desires-Intentions-Behavior model was used to examine the key predictors of reproductive decision making and behavior. A total of 320 women living with HIV were included in the current analysis. The women living with HIV were between the ages of 18 and 52 (mean=37.23, SD=7.53), 56.4% had at least one child living in the home, over 40% identified as being of African ethnicity, and the average time since HIV diagnosis was 10.49 years (SD=5.71). In hierarchical multilevel analysis, perceived family support for trying to become pregnant, living in a large metropolitan city (i.e., Toronto), women's fertility desires, and fertility intentions were associated with fertility behavior (χ(2)9=59.97, p<0.001). As only 10.6% of participants reported engaging in fertility-related behavior, while 57.5% intended a pregnancy in the future, identifying barriers to fertility and discrepancies between intentions and behaviors can support policy programs and assist health care providers to better facilitate the fertility goals of women living with HIV.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".