Implementation intention and planning interventions in Health Psychology: Recommendations from the Synergy Expert Group for research and practice
Bibliographic record
Abstract
The current article details a position statement and recommendations for future research and practice on planning and implementation intentions in health contexts endorsed by the Synergy Expert Group. The group comprised world-leading researchers in health and social psychology and behavioural medicine who convened to discuss priority issues in planning interventions in health contexts and develop a set of recommendations for future research and practice. The expert group adopted a nominal groups approach and voting system to elicit and structure priority issues in planning interventions and implementation intentions research. Forty-two priority issues identified in initial discussions were further condensed to 18 key issues, including definitions of planning and implementation intentions and 17 priority research areas. Each issue was subjected to voting for consensus among group members and formed the basis of the position statement and recommendations. Specifically, the expert group endorsed statements and recommendations in the following areas: generic definition of planning and specific definition of implementation intentions, recommendations for better testing of mechanisms, guidance on testing the effects of moderators of planning interventions, recommendations on the social aspects of planning interventions, identification of the preconditions that moderate effectiveness of planning interventions and recommendations for research on how people use plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.544 | 0.392 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.012 | 0.018 |
| Research integrity | 0.027 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".