Qualitative Study on Factors Influencing Aging Population’s Online Grocery Shopping and Mode Choice When Grocery Shopping in Person
Bibliographic record
Abstract
Given that older adults are prone to car cessation, they may also be at risk of food insecurity. Online shopping has the potential to become a key solution to this growing social issue. The objective of this study was to understand how mode use relates to food shopping patterns, and what specifically motivates older adults to choose certain travel modes for grocery shopping or to shop online. Sixty-one retired individuals were interviewed in Montreal, Canada. Participants were first asked to discuss their food shopping habits and the modes they used to purchase food. Then, participants were asked open-ended questions about beliefs from the theory of planned behavior. Participants listed advantages/disadvantages, people who approve/disapprove, and facilitating factors/barriers related to travel modes and online grocery shopping. Most participants never used online grocery shopping. Results revealed similarities in shopping styles between car drivers and online shoppers. Both were organized (prepared lists), shopped in bulk, and went on regularly timed shopping trips. Public transit (PT) and active mode users were spontaneous and irregular shoppers who viewed in-person shopping as physical and social activity opportunities. Grocery shopping using these modes could be made easier for some participants if shops offered home delivery after in-store purchases. Car drivers were more likely to adopt online services than PT or active mode users who preferred delivery after in-person shopping to reduce obstacles linked to these modes. In order for online grocery shopping to be integrated as part of one's established habits, both travel habits and grocery shopping habits must be observed jointly.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".