Quantification of the subjective labour load of a filmless radiology system by the contingent valuation method: a pilot study
Bibliographic record
Abstract
Background: A filmless radiology system, which implements a radiology information system and picture archiving and communication system, brings major changes in the work patterns of radiologists and radiological technologists. The purpose of this study was to prospectively quantify the subjective labour load of a filmless radiology system for radiologists and radiological technologists by the contingent valuation method (CVM) and to evaluate the economic labour value. Methods: The questionnaire survey included 14 radiologists and 46 radiological technologists. There was a 75% effective response rate with a total of 45 respondents. The subjective labour load of a filmless radiology system was quantified by the CVM as willingness to accept (WTA). The double-bound dichotomous choice approach was employed for the questionnaire format for WTA. Logistic regression analysis was used to identify the factors affecting WTA. Results: The median and mean WTA per month for radiologists were estimated to be 82,902 yen ($829) and 46,808 yen ($468), respectively. The median and mean WTA for radiological technologists were estimated to be 15,622 yen ($156) and 16,784 yen ($168), respectively. Logistic regression analysis showed promotional view (i.e., willingness to introduce computerization of medical information) as the only significant factor affecting WTA for radiological technologists (p < 0.05). Conclusions: The change from film-based- to filmless- radiology system would be acceptable providing that medical staff in the department of radiology think their labour changes would yield an increase of satisfaction equivalent to 1,430,000 ($14,300) -1,880,000 yen ($18,800).
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".