Assessing Health Equity in Partnership with Children’s Mental Health Organizations: Considerations Before the Implementation of Parenting Programs
Bibliographic record
Abstract
Objectives: Understanding and addressing how an individual's social, political, economic, and cultural context affects their ability to achieve optimal health is essential to designing and implementing interventions. Before evaluating two parenting programs, in partnership with four children's mental health organizations, we used the Health Equity Impact Assessment tool (HEIA) to identify groups that may experience unintended health impacts, as well as generated mitigation strategies to address these impacts. Methods: = 7), and a geographic information systems analysis. All sources of evidence were considered and analyzed using reflective thematic analysis. Summary reports were shared with all partners. Results: A range of groups were identified as at risk of experiencing unintended health impacts, including caregivers who are racialized, immigrants, Indigenous, living with mental health issues or addictions, dealing with intellectual challenges and/or low literacy levels, survivors of childhood trauma, single parent families, or families experiencing financial difficulties. Unintended health impacts were sorted into 6 main themes which fell under the overarching themes of accessibility of the programs and cultural appropriateness. Mitigation strategies as well as innovative strategies already being applied by participating organizations are discussed. Conclusion: Although this HEIA focused on parenting programs, the findings address equity issues applicable to the provision of a wide spectrum of children's mental health services.
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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 | 0.099 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".