10. Religious Commitment as a Positive Health Asset for Children: A Secondary Analysis of Qualitative Data
Bibliographic record
Abstract
"During the summer of 2017 Frances Macvicar worked with Dr. Sharday Mosurinjohn and Dr. Valerie Michaelson on a project about religious commitment as a positive health asset for young people. She performed a secondary analysis of qualitative data from over fifty adolescent boys and girls from across Canada. While, as expected, overall religious commitment was not important to many young people, for those who did find it important, its protective effects were strong. Currently, Frances Macvicar, Sharday Mosurinjohn, Valerie Michaelson, and colleagues from the Department of Public Health Sciences are co-writing a paper that treats this data in conjunction with quantitative data from another much larger study. Further directions suggested by the project include exploring ways to account for the relationships between religion, spirituality, and health in educational settings."
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".