Realistic Orientation and the Transition to Motherhood
Bibliographic record
Abstract
Current models of adjustment suggest that well-being is characterized by an optimistic, sometimes unrealistically positive self-view and worldview. In contrast, we propose that a Realistic Orientation—giving frequent thought to both positive and negative possibilities—better prepares people for difficult events. Two studies are presented assessing the effect of a Realistic Orientation in the context of the transition to motherhood. In Study 1, 181 women pregnant with their first child were assessed on their orientation to motherhood, optimism, and depression. Regression analyses indicate that a Realistic Orientation significantly predicts adjustment over and above optimism. In Study 2, 69 women expecting their first child were interviewed pre- and postpartum. Relative to those categorized as Positively- or Negatively-Oriented, those women categorized as Realistically-Oriented reported greater decreases in depressive symptoms from prepartum to postpartum. When the transition was accompanied by many unexpected negative surprises, those who had thought more about negative possibilities prepartum showed a decrease in depressive symptoms whereas those who had not given as much thought to the negative possibilities showed an increase in depressive symptoms. The data converge to suggest that an orientation to future events that includes frequent thoughts of both positive and negative possible outcomes promotes resilience in the face of adversity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".