The Family Stress Model in a representative Canadian sample: A network analysis perspective
Bibliographic record
Abstract
Mental health challenges have traditionally been viewed in categories; however, more recently, researchers have begun to view psychopathology as a network of symptoms (Boorsboom & Cramer, 2013). In addition to the connection between symptoms, psychopathology is influenced by many contextual factors. The Family Stress Model explains how environmental factors–including economic hardship, family functioning, stressors, and social support–play a role in psychological distress (Masarik & Conger, 2017). However, less is known about how these risk and protective factors fit into a network of psychopathology. Therefore, the current study will explore the network of indicators of positive and negative mental health, as well as their connection to contextual factors, including neighbourhood income, social support, negative social interactions, life stressors, COVID stressors, and childhood adversity. Furthermore, the proposed study will investigate whether these relationships change based on age, gender, and neighbourhood income. References Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608 Masarik, A. S., & Conger, R. D. (2017). Stress and child development: A review of the Family Stress Model. Current Opinion in Psychology, 13, 85–90. https://doi.org/10.1016/j.copsyc.2016.05.008
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".