Toward Equitable Access to Psychosocial Screening in Canada: Validation of the French–Canadian Psychosocial Assessment Tool
Bibliographic record
Abstract
OBJECTIVE: The Psychosocial Assessment Tool (PAT) is a brief caregiver report, family-centered, psychosocial risk screening tool widely used in pediatrics and available in many languages. Although French is an official language of Canada, a French-Canadian version of the PAT has not yet been validated, which impedes access to this tool for family psychosocial screening. This study aimed to translate, adapt as necessary, and validate the French-Canadian version of the PAT. METHODS: The PAT 3.0 was translated into French using the forward-backwards method. Interviews with healthcare workers (n = 5) and a focus group of parents of children newly diagnosed with cancer (n = 4) led to minor modifications to improve cultural adaptation and comprehensibility. Subsequently, 66 French-speaking parents of children newly diagnosed with cancer participated in a quantitative validation study; completing the French-Canadian PAT and related caregiver measures. RESULTS: The French-Canadian PAT has an overall internal consistency of KR-20 = 0.64, with subscales ranging from 0.57 to 0.86. The total PAT score significantly correlated with the Distress Thermometer (r = 0.54). Congruent validity was demonstrated for most PAT subscales, except for Family Beliefs and Family Problems. The sample followed this distribution on the Pediatric Psychosocial Preventative Health Model: 21.2% universal, 53% targeted, and 25.8% clinical. CONCLUSION: The French-Canadian PAT provides insight into family psychosocial risk. This study is a step toward equitable access to care by providing French-speaking families with access to a tool aligned with the psychosocial standards of care in pediatric oncology. Future research should focus on implementing the PAT in Canadian clinical settings.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".