The experience of effort in ADHD: a scoping review
Bibliographic record
Abstract
Background: Mental effort plays a critical role in regulating cognition. However, the experience of mental effort may differ for individuals with Attention-Deficit/Hyperactivity Disorder (ADHD), a disorder for which sustained mental effort 'avoidance' or 'dislike' is a criterion in the DSM. We conducted a scoping review to characterize the literature on the experiences of effort in ADHD. Methods: This systematic scoping review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Extension for Scoping Reviews and Joanna Briggs Methodology. PsycINFO (OVID), PsycINFO (ProQuest) and PubMed were searched for studies published in English before February 14, 2023. Studies must have included an ADHD population or a measure of ADHD symptomatology, in addition to a self-report measure of the experience of effort or the use of an effort preference paradigm. Two researchers reviewed all abstracts, and one researcher reviewed full-text articles. Results: Only 12 studies met the inclusion criteria. Several gaps and inconsistencies in the research were identified in terms of method, definitions of effort, measurements of ADHD, and sample characteristics. Moreover, the pattern of results on the experience of effort was mixed. Conclusion: Despite its diagnostic and conceptual significance, the experience of mental effort in ADHD is not well studied. Critical gaps were identified in the existing literature. A three-facet conceptualization of effort is proposed-specifically, task-elicited effort, volitionally exerted effort, and the affect associated with engaging in effort - to guide future explorations of the experience of effort in ADHD.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".