Exploring value co-creation in public health: factors influencing collaborative development of a food retailers’ toolkit from multi-stakeholder perspectives
Bibliographic record
Abstract
In public health, collaborative efforts aimed at improving food environments have gained momentum, yet the subtleties of value co-creation remain underexamined. Value co-creation is defined as the shared value generated by a group of participants through interactions. This study explores the perspectives of multiple stakeholders involved in developing a Toolkit designed to assist food retailers in implementing healthy food policies. Six semi-structured interviews were conducted with collaborators between August and September 2023 and analyzed using thematic analysis guided by a value co-creation framework. The interview data yielded 12 subthemes, grouped into three themes for reporting: 1) partner engagement, which includes factors fostering an interactive atmosphere; 2) partner capabilities, reflecting the networks, skills, and knowledge essential for successful collaboration; and 3) partner experience, which emphasizes the benefits and learning valued during the Toolkit’s development. Results showed that effective communication, transparency, and leadership are key drivers of collaborative success, whereas challenges included workload management and the need for clear governance structures. Comparisons with existing literature highlight the importance of these factors in fostering sustainable partnerships within public health initiatives. The findings stress the need for organizations to adopt co-creation as standard practice and recommend establishing strong frameworks to support ongoing collaboration. Additionally, further research is needed to deepen the understanding of the complexities involved in multi-partner interactions and their implications for policy implementation in food retail settings. This study offers valuable insights to enhance future public health initiatives through effective co-creation strategies.
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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 | 0.045 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.004 |
| 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".