Exploring the barriers and facilitators of continuous quality improvement for health promotion within healthcare food environments
Bibliographic record
Abstract
Continuous quality improvement (CQI) has become a widely accepted approach to optimize health services while lowering healthcare costs (Quintuple Aim) and has expanded from clinical interventions to health promotion. Retail food environments (e.g. hospital cafeterias, cafes) are of interest given the increased adoption of healthy eating policies and interventions to influence diet (e.g. price, promotion, placement and product). However, there is a lack of understanding of what organizational and policy processes are necessary to implement CQI for health promotion in healthcare. This research uses a qualitative multiple exploratory case study design to explore the barriers and facilitators of CQI for health promotion in healthcare retail food environments. This research occurred in a healthcare setting with an organizational Healthy Eating Policy applicable to staff, patients and visitors. We collected semi-structured interview data with 12 healthcare staff working in Nutrition & Food Services in a Canadian provincial health authority from January to June 2023. We used directed content analysis to analyze the data. We used the Inside out model to interpret cross-cutting organizational barriers and facilitators. Four cases of quality improvement interventions (Plan-Do-Study-Act (PDSA) cycles) were identified. Barriers included expertise to interpret nutrient criteria, lack of data, conflicting benchmarks (e.g. finance and health), third-party vendors, past negative experiences, and a lack of time to monitor and evaluate. Facilitators included an organizational Healthy Eating Policy, understanding community context, local knowledge, partnerships with researchers and leadership. This study revealed how overarching policies, accompanied by organizational support, facilitated quality improvement and engagement in CQI but also created barriers to routine practice and sustainability of health-promoting interventions.
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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 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".