The development and validation of a shorter version of the CANHELP Lite
Bibliographic record
Abstract
Historically, improving EOL care has been hampered by a lack of validated tools to measure quality EOL care. Recently, we developed a novel measurement tool, theCANadianHealth careEvaLuationProject (CANHELP) questionnaire, which can be used to assess both patient and family satisfaction with EOL care. Whilst this instrument is reliable, valid, and comprehensively measures the patient (and families) experience with EOL, it takes 40-60 minutes to complete. The length of the interview may preclude its uptake; a shorter version would be more desirable. The purpose of this study is to develop and begin to validate a shorter version of the CANHELP questionnaire. Data were collected by a cross-sectional survey of patients (n=361) with advanced illnesses and their family members (n=255), who completed the long version of CANHELP, a global rating of satisfaction (GRS), FAMCARE (family members only), and a quality of life (QOL) questionnaire. We reduced the items on the long version of CANHELP based on derived importance of individual items and their relationship to global ratings of overall satisfaction, the frequency of missing data, the distribution of responses and focus groups with front line users. With the remaining items, we evaluated construct validity by describing the correlation of the new CANHELP Lite with full version of CANHELP, GRS, FAMCARE, and the QOL questionnaire scores and found it to be satisfactory. The CANHELP Lite questionnaire is a shorter version of the full CANHELP instrument and is a valid instrument to measure satisfaction with EOL care.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".