Creation and Initial Validation of the Caregiver Analysis of Reported Experiences with Swallowing Disorders (CARES) Screening Tool
Bibliographic record
Abstract
Purpose Dysphagia is a debilitating condition with widespread consequences. Previous research has revealed dysphagia to be an independent predictor of caregiver burden. However, there is currently no systematic method of screening for or identifying dysphagia-related caregiver burden. The aim of this study was to develop a set of questions for a dysphagia-related caregiver burden screening tool, the Caregiver Analysis of Reported Experiences with Swallowing Disorders (CARES), and pilot the tool to establish preliminary validity and reliability. Method The questionnaire was developed through an iterative process by a team of clinical researchers with expertise in dysphagia, dysphagia-related and general caregiver burden, and questionnaire design. A heterogenous group of 26 family caregivers of people with dysphagia completed the CARES, along with the Eating Assessment Tool (EAT-10), the International Dysphagia Diet Standardisation Initiative Functional Diet Scale (IDDSI-FDS), and the Zarit Burden Interview (ZBI). Information on construct validity, item fit, convergent validity, internal consistency, and reliability was determined via Rasch analysis model testing, Cronbach's alpha, and Spearman's rho calculations. Results The final CARES questionnaire contained 26 items divided across two subscales. The majority of the questionnaire items fit the model, there was evidence of internal consistency across both subscales, and there were significant relationships between dysphagia-specific burden (CARES) and perceived swallowing impairment (EAT-10), general caregiver burden (ZBI), and diet restrictiveness (IDDSI-FDS). Conclusions Results from the current study provide initial support for the validity and reliability of the CARES as a screening tool for dysphagia-related burden, particularly among caregivers of adults with swallowing difficulties. While continued testing is needed across larger groups of specific patient populations, it is clear that the CARES can initiate structured conversations about dysphagia-related caregiver burden by identifying potential sources of stress and/or contention. This will allow clinicians to then identify concrete methods of reducing burden and make appropriate referrals, ultimately improving patient care.
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 | 0.045 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".