Effectiveness of goal management training® in improving executive functions: A meta-analysis
Bibliographic record
Abstract
Our objective was to review the literature and quantitatively summarise the effectiveness of Goal Management Training® (GMT) (alone or in combination with other training approaches) in improving executive functions in adult populations. Ovid, Scopus, Web of Science, and ProQuest Dissertations & Theses Global were searched for articles citing “goal management training”. Any group trials (n > 3) in adults that used multiple-session GMT programmes were included in the analyses. Outcome variables were extracted and classified into one of nine cognitive measures domains: executive functioning tasks, everyday executive functioning tasks, subjective executive tasks rated by the patient, subjective executive tasks rated by proxy, working memory, speed of processing, long-term memory, instrumental activities of daily living and general mental health status questionnaires. A total of 21 publications, containing 19 separate treatment group samples were included in the final analyses. Significantly positive small to moderate effect sizes were observed in all cognitive measure domains (except speed of processing) with effects maintained at follow-up assessments for all followed-up outcome measures, except for subjective ratings by patients and proxy. The analysis suggests that GMT is an effective intervention, leading to moderate improvements in executive functions that are usually maintained at follow-up.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".