Motivation for COVID-19 Vaccination: Applying a Self-Determination Theory Perspective to a Global Health Crisis
Bibliographic record
Abstract
Examining the spectrum of vaccine attitudes within the general public, spanning from hesitancy to confidence, is pivotal in addressing the challenges posed by the COVID-19 pandemic. Despite widespread campaigns advocating for vaccine uptake, a proportion of the population harbour reservations about the safety and efficacy of vaccines. This study seeks to explore the determinants of vaccine attitudes in Canada, leveraging key concepts from the well-established Self-Determination Theory (SDT), including basic psychological needs and the quality of an individual’s motivation. During a crucial juncture in the COVID-19 pandemic (December 2021), 292 participants were recruited and completed an online survey assessing levels of satisfaction/frustration of basic psychological needs (sense of autonomy, relatedness, and competence), vaccine attitudes (confidence and hesitancy), and motivation towards vaccination (controlled and autonomous). Two mediation models were employed to examine whether autonomous-controlled motivation mediated the relationship between need satisfaction-frustration and vaccine attitudes. Model 1 revealed a full mediating effect, indicating that need satisfaction influenced vaccine confidence only through autonomous motivation (ab1 = 0.09, SE = 0.04, z = 2.19, 95 % CI [0.01, 0.18]). Meanwhile, Model 2 demonstrated that need frustration was associated with vaccine hesitancy partially through controlled motivation (ab2 = 0.05, SE = 0.02, z = 2.54, 95 % CI [0.02, 0.10]). These findings underscore the applicability of SDT in investigating the motivational mechanisms that shape vaccine attitudes. Recognizing psychosocial factors, including the balance of basic needs and quality of motivations, may be integral to informing effective public health 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".