A classification of motivation and behavior change techniques used in self-determination theory-based interventions in health contexts.
Bibliographic record
Abstract
While evidence suggests that interventions based on self-determination theory have efficacy in \nmotivating adoption and maintenance of health-related behaviors, and in promoting adaptive \npsychological outcomes, the motivational techniques that comprise the content of these \ninterventions have not been comprehensively identified or described. The aim of the present \nstudy was to develop a classification system of the techniques that comprise self-determination \ntheory interventions, with satisfaction of psychological needs as an organizing principle. \nCandidate techniques were identified through a comprehensive review of self-determination \ntheory interventions and nomination by experts. The study team developed a preliminary list of \ncandidate techniques accompanied by labels, definitions, and function descriptions of each. \nEach technique was aligned with the most closely-related psychological need satisfaction \nconstruct (autonomy, competence, or relatedness). Using an iterative expert consensus \nprocedure, participating experts (N=18) judged each technique on the preliminary list for \nredundancy, essentiality, uniqueness, and the proposed link between the technique and basic \npsychological need. The procedure produced a final classification of 21 motivation and \nbehavior change techniques (MBCTs). Redundancies between final MBCTs against techniques \nfrom existing behavior change technique taxonomies were also checked. The classification \nsystem is the first formal attempt to systematize self-determination theory intervention \ntechniques. The classification is expected to enhance consistency in descriptions of selfdetermination theory-based interventions in health contexts, and assist in facilitating synthesis \nof evidence on interventions based on the theory. The classification is also expected to guide \nfuture efforts to identify, describe, and classify the techniques that comprise self-determination \ntheory-based interventions in multiple domains.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".