A Qualitative Systematic Review of Public–Private Partnership in Promoting Physical Activity
Bibliographic record
Abstract
The purpose of the study is to conduct a comprehensive review of public-private partnership (PPP) literature that pertains to promoting physical activity. A qualitative systematic review guided data search and screening process, and the findings were synthesized and interpreted using a qualitative content analysis method. Literature was searched from 16 academic and 6 gray literature databases. A total of 1,117 articles were initially searched, full texts of 186 articles were assessed, and 13 articles that met the inclusion criteria were finally included. PPPs have been initiated in various contexts including implementing the pledge policy, program coordination, and infrastructure supports. Public-sector partners were identified in a range of vertical and horizontal levels. Private partners were mainly manufacturers and/or retailers related to physical activity, sport facility operators, professional sport teams, or companies for providing infrastructures for active transportation. Public and private organizations have performed various roles of funding the initiatives, developing and implementing diverse resources, and taking actions to deliver benefits to the communities. Several challenges were reported when developing, implementing, and evaluating the partnership initiatives. The outcomes of the current review can be utilized to anticipate pragmatic issues when public and private partners jointly participate in physical activity promotion.
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 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 | 0.065 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".