Valuation of Lost Productivity in Caregivers: A Validation Study
Bibliographic record
Abstract
Objective This study aimed to: (a) adapt the previously validated Valuation of Lost Productivity (VOLP) questionnaire for people with health problems, to a caregiver version to measure productivity losses associated with caregiving responsibilities, and (b) evaluate measurement feasibility and validity of an online version of the caregiver VOLP questionnaire. Methods A mixed methods design was utilized. Qualitative methods, such as reviewing existing questionnaires that measured caregiver work productivity losses and performing one-on-one interviews with caregivers, were used for VOLP adaptation and online conversion. Quantitative methods were used to evaluate feasibility and validity of the online VOLP. The Work Productivity and Activity Impairment (WPAI) questionnaire for caregivers was included to compare its absenteeism and presenteeism outcomes and their correlations with VOLP outcomes. Results When adapting the VOLP for caregivers, our qualitative analysis showed the importance of adding three major components: caregiving time, work productivity loss related to volunteer activities and caregivers’ lost job opportunities. A total of 383 caregivers who completed online survey were included in our final quantitative analysis. We found small Spearman rank correlations between VOLP and WPAI, observing a larger correlation between their absenteeism [ r = 0.49 (95% confidence interval: 0.37–0.60)] than their presenteeism [ r = 0.36 (0.24–0.47)]. Correlations between VOLP outcomes and total caregiving hours were larger for absenteeism [ r = 0.38 (0.27–0.47)] than presenteeism [ r = 0.22 (0.10–0.34)]. Correlations between WPAI outcomes and total caregiving hours were smaller for absenteeism [ r = 0.27 (0.15–0.38)] than presenteeism [ r = 0.35 (0.23–0.46)]. Conclusion The study provides evidence of the feasibility and preliminary validity evidence of the adapted VOLP caregiver questionnaire in measuring productivity losses due to caregiving responsibilities, when compared with the results for WPAI and the results from the previous patient-VOLP validation study.
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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 | 0.036 | 0.081 |
| 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 | 0.001 |
| 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".