Validating the Children’s Intrinsic Needs Satisfaction Scale in the 2019 Canadian Health Survey on Children
Bibliographic record
Abstract
Background: The Public Health Agency of Canada monitors the psychological and social well-being of Canadian youth using the Children's Intrinsic Needs Satisfaction Scale (CINSS). Validation analyses of the CINSS have been conducted, but not in the 2019 Canadian Health Survey on Children and Youth (CHSCY), a more recent and representative national survey with a different sampling frame, collection method and other measured outcomes. This study tested the validity of the CINSS in the 2019 CHSCY. Data and methods: Data were collected in all provinces and territories from February 11 to August 2, 2019. The CINSS was administered to respondents aged 12 to 17 years and was designed to assess relatedness, autonomy and competence at home, at school and with friends. Descriptive statistics for CINSS items and subscales were obtained. Confirmatory factor analysis (CFA) was conducted to test how well a correlated traits correlated uniqueness (CTCU) model fit the CINSS data. Associations with mental health and other psychosocial variables were examined. Results: In general, items within the CINSS were correlated in expected ways, and support was found for a CTCU model in the CFA. While response distributions on the CINSS items were skewed, the CINSS subscales had acceptable internal consistency and were associated with self-rated mental health, happiness, life satisfaction, perceived stress, bullying victimization and behaviour problems in line with expectations. Interpretation: This study supports the validity of the CINSS. Inclusion of the CINSS in future youth health surveys would allow for continued public health surveillance of the psychological and social well-being of youth in Canada.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".