Intergenerational Cooperation at Work to Promote Inclusion and Healthy Participation of Older Workers in the Foodservice Sector: Protocol for a Qualitative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Increasing intergenerational coexistence in the job market may heighten the risk of intergenerational conflicts, which can negatively impact the inclusion and healthy participation of older workers. The foodservice sector is characterized by significant intergenerational coexistence, reinforced by the presence of student workers. According to the literature, practices promoting intergenerational cooperation can reduce the risk of conflict and contribute to a healthier work climate. OBJECTIVE The main objective of this research project is to propose a prioritized list of intergenerational cooperation practices to promote the inclusion and healthy participation of older workers in the foodservice sector. METHODS This project will be carried out in 2 stages. The first stage will use a descriptive interpretative design based on the photo-elicitation method to identify intergenerational cooperation practices from the perspective of older workers. The second stage will use the Technique for Research of Information by Animation of a Group of Experts method to obtain a group consensus among professionals, managers, and researchers to prioritize the identified practices. RESULTS The first stage of this project is underway. The study has been funded by the Quebec Network for Research on Aging since September 2024. Participant recruitment began at the end of January 2025, following ethics committee approval, and ended in May 2025. In total, 12 participants were recruited for stage 1. The data collected are currently being analyzed, and the results are scheduled to be submitted for publication in 2026. CONCLUSIONS On a theoretical level, this project will propose a list of practices promoting intergenerational cooperation at work in the foodservice sector, giving a voice to older workers to better understand their perspectives. This project will also offer a prioritization of those practices, based on the perspective of different actors (researchers, professionals, and managers of the foodservice sector). On a practical level, this knowledge will help the actors of the foodservice sector to integrate these practices into their organizations to promote the inclusion and healthy participation of older workers. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/79012
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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 | 0.064 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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".