Examining the unanticipated effects of public-private partnerships for preventing chronic disease
Bibliographic record
Abstract
Background Despite their potential value, the application of public-private partnerships to chronic disease prevention (CDP) is relatively new and also contested. In 2013, the Public Health Agency of Canada (PHAC) launched a Multi-sectoral Partnerships initiative, engaging organizations from public and private sectors in CDP activities. This study was completed as part of the initiative’s Learning and Improvement Strategy, and aimed to examine the unanticipated effects (both positive and negative) of public-private partnerships for CDP. Methods PHAC’s Multi-sectoral Partnerships initiative supports more than 30 partnership projects, each engaging 3-15 organizations, with matched funding from private sector partners (project budgets vary between CAD$0.3 to 9 million). Using a multiple case study design, 13 semi-structured interviews were conducted with staff from each organization in 3 diverse partnership projects. Projects were selected to ensure variability in the number of partners, area of focus, and stage of partnership development. Transcribed interviews were analyzed thematically. Results Multiple unanticipated effects were identified and organized into 4 themes: (1) increased flexibility and responsiveness of government; (2) accessibility of new resources (people, skills, expertise); (3) building new capacities; and (4) delays in project timelines. These effects were influenced by many factors, such as historical interactions, organizational accountabilities, and differing partner expectations. Conclusions The majority of unanticipated effects identified by those in this study were considered positive, such as building new capacities and accessing new resources. Other effects suggest potential areas for exploration, such as reviewing procedures related to monitoring and approval processes. Findings from this work are informing the Agency’s planning related to partnership brokering, monitoring, evaluation and continuous improvement practices. Key messages: Public-private partnerships in public health may unexpectedly influence the actions of partners, their access to resources, build capacities in unanticipated domains, and surface unexpected tensions Understanding these unanticipated effects may assist various stakeholders in improving partnership evaluation strategies, partnership brokering procedures or continuous quality improvement practices
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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 | 0.100 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".