Factors influencing intentions to transition to plant‐based protein diets: Canadian perspective
Bibliographic record
Abstract
There is a pressing need for healthy diets guided by environmental and nutritional targets. Plant-based proteins have emerged as a recent and rapidly growing trend in response to the challenge of sustainable and healthy food systems. While plant-based protein foods are widely promoted as sustainable alternatives, shifting beliefs and attitudes about conventional protein sources present an ongoing challenge. The study examined Canadians' intentions to transition to plant-based protein diets, partially or entirely. A nationally representative survey was conducted among Canadian consumers to achieve our research objective. The survey was administered online using the Qualtrics platform by a market research firm and yielded valid responses from over 1800 participants. The Theory of Planned Behavior (TPB) constructs-attitudes, self-efficacy, and perceived availability-explained only 12% of the variation in intentions toward plant-based protein foods, while sustainability and ethical concerns accounted for 10% of the variation in dietary patterns. Meat attachment negatively impacted changes in dietary patterns, explaining 11% of the intention variation. Additionally, individual past behavior accounted for 7% of intentions toward plant-based proteins. Demographic factors, such as gender and education, strongly and positively predicted purchase intentions, while contextual factors, such as residing in rural neighborhoods and being from Atlantic Canada, showed a strong negative association with intentions toward plant-based protein diets. The findings underscore the multifaceted nature of individuals' intentions toward plant-based protein diets and emphasize the significance of considering cognitive, social, emotional, and past behavioral factors, alongside sustainability values and messaging, to transition to a more plant-based protein diet. This approach should carefully balance individuals' emotional connection and the perception of meat as essential to their meals. Also, targeting interventions based on demographic characteristics, specifically gender, education, and residential neighborhood, can enhance changes in dietary protein sources. The findings contribute to the existing body of knowledge on consumer behavior and sustainable diets, guiding future research and policies informing the design of effective interventions to promote plant-based protein consumption and dietary changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".