Autonomy‐supportive intervention: an evolutionary concept analysis
Bibliographic record
Abstract
AIM: This paper is a report of an analysis of the concept of an autonomy-supportive intervention. BACKGROUND: A large proportion of chronic illnesses can be prevented by positive health behaviour changes. The aim of an autonomy-supportive intervention is to increase perceived autonomy support, which, in turn, increases positive health behaviour changes. Its known core components are choice, rationale and empathy. Identifying and analysing the antecedents, attributes and consequences of an autonomy-supportive intervention will increase the clarity of this concept. DESIGN: Concept analysis. DATA SOURCES: Sources were 63 papers describing an autonomy-supportive intervention in health behaviour changes indexed in CINAHL, PsycINFO and MEDLINE (all dates until July 2012). METHODS: Rodgers' evolutionary method of concept analysis was used to help identify and analyse the antecedents, attributes and consequences of the concept. RESULTS: More evolution was found in the disciplines of nursing and psychology compared with medicine in relation to the use of an autonomy-supportive intervention in theoretical frameworks. The antecedents included assessment prior to intervention delivery, intervention providers' beliefs, and skills training. A lack of homogeneity in the manner in which the attributes were described was found in the literature across disciplines and the attributes were classified under five components instead of three: choice, rationale, empathy, collaboration and strengths. CONCLUSION: An autonomy-supportive intervention is a useful concept across healthcare disciplines and future research should aim at identifying which attributes and components of an autonomy-supportive intervention may be more effective in increasing perceived autonomy support.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".