Differences between Only Children and Children with 1 Sibling Referred to a Psychiatric Clinic: A Test of Richards and Goodman's Findings
Bibliographic record
Abstract
OBJECTIVE: To test Richards and Goodman's hypothesis that a higher proportion of only children under age 5 years assessed in a psychiatric department do not present a psychiatric diagnosis, compared with preschool children with 1 sibling, and to investigate other variables relative to children in this age group with no psychiatric disorder, in light of Richards and Goodman's findings. METHOD: We gathered data from 169 children under age 5 years seen in the psychiatric department of a large pediatric hospital in Montreal, Quebec. RESULTS: First, bivariate analysis showed no differences between the proportion of only children and children with 1 sibling regarding absence of a psychiatric diagnosis. Second, multivariate logistic regression analysis revealed that child's age and mother's child-rearing attitudes were significant variables. Younger children (that is, age 0 to 2 years) and children whose mothers had "adequate" child-rearing attitudes (that is, not exhibiting significant impatience, rejection, stubbornness, neglect, or overprotectiveness) were more likely to have no disorder. CONCLUSION: These findings run counter to Richards and Goodman's results and suggest that other variables, such as child's age and mother's behaviour, are significant predictors of children under age 5 years having no diagnosis.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".