Supporting Ontario public health units to address adverse childhood experiences in pandemic recovery planning: A priority-setting exercise
Bibliographic record
Abstract
BACKGROUND: Adverse childhood experiences (ACEs) are potentially traumatic exposures experienced during childhood, for example, neglect. There is growing evidence that the coronavirus disease 2019 (COVID-19) pandemic and related socioeconomic conditions contributed to an increased risk of ACEs. As public health programs/services are re-evaluated and restored following the state of emergency, it is important to plan using an ACEs-informed lens. The aim of this study was to identify and prioritize initiatives or activities that Public Health Ontario (PHO) could undertake to support Ontario public health units' work towards ACEs-informed pandemic recovery plans. METHODS: The Child Health and Nutrition Research Initiative method was adapted to conduct a priority-setting exercise (May-October 2022). Two online surveys were administered with members of the Healthy Growth and Development (HGD) Evidence Network, comprised of public health unit staff working in child and family health/HGD from Ontario's 34 public health units. In the first survey, participants were asked to propose activities or initiatives that PHO could undertake to support Ontario public health units' work towards ACEs-informed planning. In the second survey, participants were asked to score the final list of options against pre-determined prioritization criteria (for example, relevance). Responses were numerically coded and used to calculate prioritization scores, which were used to rank the options. RESULTS: In all, 76% of public health units (n = 26) responded to the first survey to identify options. The 168 proposed ideas were consolidated into a final list of 13 options, which fall under PHO's scientific and technical support mandate areas (data and surveillance, evidence synthesis, collaboration and networking, knowledge exchange and research). A total of 79% of public health units (n = 27) responded to the follow-up survey to prioritize options. Prioritization scores ranged from 76.4% to 88.6%. The top-ranked option was the establishment of a new provincial ACEs community of practice. CONCLUSIONS: Over three quarters of public health units contributed to identifying and ranking 13 options for PHO to support public health units in considering and addressing ACEs through pandemic recovery planning. In consultation with the ACEs and Resilience Community of Practice, recently formed on the basis of this exercise, PHO will continue to use the ranked list of options to inform work-planning activities/priorities.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".