Speech Acts as a Window to the Difficulties in Instrumental Activities of Daily Living: A Qualitative Descriptive Study in Mild Neurocognitive Disorder and Healthy Aging
Bibliographic record
Abstract
Background: Executive functions (EF) are central to instrumental activities of daily living (IADL). A novel approach to the assessment of the impact of EF difficulties on IADL may be through the speech acts produced when performing IADL-inspired tasks in a laboratory-apartment. Speech acts may act as a window to the difficulties encountered during task performance. Objective: We aim to 1) qualitatively describe the speech acts produced by participants with mild neurocognitive disorder (mild NCD) and healthy controls (HC) as they performed 4 IADL-inspired tasks in a laboratory-apartment, and to then 2) compare their use in both groups. Methods: The participants' performance was videotaped, and speech acts produced were transcribed. Qualitative description of all speech acts was performed, followed by a deductive-inductive pattern coding of data. Statistical analyses were performed to further compare their use by mild NCD participants and HC. Results: Twenty-two participants took part in the study (n mild NCD = 11; n HC = 11). Meta-categories of data emerged from pattern coding: strategies, barriers, reactions, and consequences. Mild NCD participants used significantly more strategies and barriers than did HC. They were more defensive of their performance, and more reactive to their difficulties than HC. Mild NCD participants' verification of having completed all tasks was less efficient than controls. Conclusions: An assessment of speech acts produced during the performance of IADL-inspired tasks in a laboratory-apartment may allow to detect changes in the use of language which may reflect EF difficulties linked to cognitive decline.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".