Supporting public health practice in healthy growth and development in the Province of Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: As public health services are modernized in Ontario, Canada, there is a need to inform the system-level roles and responsibilities of government agencies. The aim of this study was to identify how Public Health Ontario (PHO) can optimally support evidence-based planning and programming in Healthy Growth and Development (HGD) across Ontario. METHODS AND DESIGN: A situational assessment was conducted with key informants from public health and other HGD fields. SAMPLE: Key informants were identified using purposeful snowball sampling and included public health nurses, health promoters, and medical officers of health. Analytic strategy: Twenty telephone interviews and seven focus groups were used to collect data. A thematic analysis was conducted concurrently with data collection. RESULTS: Five themes were identified: (a) Transition to the new Ontario Public Health Standards (OPHS) included experiences of adopting the new OPHS within local public health units (PHUs). (b) Collaborating and networking referred to the ability to work with community partners. (c) Data, evidence, and research described the presence of data, evidence, and research to support practice. (d) Decision making, planning, and priority setting described resources available that influenced decision making. (e) Current and emerging issues in HGD included high-priority topics. CONCLUSION: Public health practice in HGD is complex with many challenges in data and evidence, and making programming decisions without adequate or measurable indicators. A specialized position at PHO is an opportunity to support some of these system-wide needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".