How well are implementation strategies and target healthcare professional behaviors reported? A secondary analysis of 204 implementation trials using the TIDieR checklist and AACTT framework
Bibliographic record
Abstract
BACKGROUND: Clear specification and reporting of implementation strategies and their targeted healthcare professional behaviors are essential for replication, adaptation, and cumulative learning in implementation science. However, critical gaps remain in the consistent use of reporting frameworks. This study aimed to: (1) assess the completeness of implementation strategy reporting using the Template for Intervention Description and Replication (TIDieR) checklist; (2) examine trends in implementation strategy reporting over time; and (3) assess the completeness of the reporting of healthcare professional behaviors targeted for change using the Action, Actor, Context, Target, Time (AACTT) framework. METHODS: We conducted a secondary analysis of 204 trials included in a systematic review of implementation strategies aimed at changing healthcare professional behavior. Implementation strategies were assessed using the 12-item TIDieR checklist; target behaviors were characterized using the five AACTT domains. Two independent reviewers extracted and coded the data. Descriptive statistics were used to summarize reporting patterns. Data were synthesized narratively and presented in tables, with trends illustrated via a scatterplot. RESULTS: Assessment of implementation strategy reporting using TIDieR showed that procedural details (98%), materials used (95%), and modes of delivery (88%) were frequently reported. Critical elements such as strategy tailoring (28%), fidelity assessment (19% planned; 17% actual), and modifications (10%) were often missing. A modest improvement in reporting was observed after the publication of TIDieR, with median scores increasing from 15.0 (IQR: 13.0-16.0) pre-2014 to 16.0 (IQR: 15.0-18.0) post-2014. Assessment of target healthcare professional behavior reporting using AACTT indicated that actions (e.g., "assess illness") and actors (e.g., nurses) were generally well reported at a high level. However, key contextual and temporal details were largely absent. While physical context was documented in all studies, the emotional and social contexts of behaviors were rarely reported. Crucial information on the duration, frequency, and period of behaviors was rarely reported. CONCLUSIONS: Implementation strategies and target behaviors are not consistently or sufficiently reported in trials. Increased adoption of structured reporting tools such as TIDieR and AACTT is essential to enhance transparency. Incorporating these frameworks during protocol development could strengthen intervention evaluation and reporting, advancing implementation science and fostering cumulative knowledge. TRIAL REGISTRATION: PROSPERO CRD42019130446.
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 | 0.371 | 0.644 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".