Running head: REPRODUCTIVE INTENTIONS The Road to Parenthood is Paved with Intentions: Incorporating Anticipated Regret into the Theory of Planned Behaviour to Explain Reproductive Intentions
Bibliographic record
Abstract
Demographic trends in Canada are changing, resulting in an aging population. The current birth rate has decreased to a level of 1.6 children per woman, which is below the replacement rate of 2.1 children per woman and the desired birth rate of 2.7 children per woman. Understanding how people make reproductive decisions is important in gaining an understanding of these changing trends. The Theory of Planned Behaviour (TPB) is a theoretical model designed to predict and explain intentional behaviour. According to the TPB, behaviours are influenced by the beliefs underlying behaviour. These beliefs are categorized as attitudinal beliefs, social normative beliefs, and personal control beliefs. Focus groups were conducted with 22 undergraduate university students in order to examine the applicability of the TPB in regards to fertility intentions and to understand the attitudinal, normative, and control beliefs of emerging adults regarding having children or remaining childfree. The concept of anticipated regret was included in the discussion questions as it has been shown to be a powerful affective determinant of behaviour but has not been examined in regards to reproductive intentions. Examination of behavioural, normative, and control beliefs about parenting and remaining childfree identified
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".