Swimming against the tide: A Canadian qualitative study examining the implementation of a province-wide public health initiative to address health equity
Bibliographic record
Abstract
BACKGROUND: Effectively addressing the social determinants of health and health equity are critical yet still-emerging areas of public health practice. This is significant for contemporary practice as the egregious impacts of health inequities on health outcomes continue to be revealed. More public health organizations seek to augment internal organizational capacity to address health equity while the evidence base to inform such leadership is in its infancy. The purpose of this paper is to report on findings of a study examining key factors influencing the development and implementation of the social determinants of health public health nurse (SDH-PHN) role in Ontario, Canada. METHODS: A descriptive qualitative case study approach examined the first Canadian province-wide initiative to add SDH-PHNs to each public health unit. Data sources were documents and staff from public health units (i.e., SDH-PHNs, Managers, Directors, Chief Nursing Officers, Medical Officers of Health) as well as external stakeholders. Data were collected through 42 individual interviews and 226 documents. Interview data were analyzed using framework analysis methods; Prior's approach guided document analysis. RESULTS: Three themes related to the SDH-PHN role implementation were identified: (1) 'Swimming against the tide' to lead change as staff navigated ideological tensions, competency development, and novel collaborations; (2) Shifting organizational practice environments impacted by initial role placement and action to structurally embed health equity priorities; and (3) Bridging policy implementation gaps related to local-provincial implementation and reporting expectations. CONCLUSIONS: This study extends our understanding of the dynamic interplay among leadership, change management, ideological tensions, and local-provincial public health policy impacting health equity agendas. Given that the social determinants of health lie outside public health, collaboration with communities, health partners and non-health partners is essential to public health practice for health equity. The study findings have implications for increasing our knowledge and capacity for effective system-wide intervention towards health equity as a critical strategic priority for public health and for broader public policy and community engagement. Appropriate and effective public health leadership at multiple levels and by multiple actors is tantamount to adequately making inroads for health equity.
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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 | 0.036 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".