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Record W2063603913 · doi:10.1017/s1460396915000059

How do patients receiving radiotherapy in a Dutch hospital value their time? A contingent valuation study

2015· article· en· W2063603913 on OpenAlexaff
France Portrait, Marjolein Bakker, Ben J. Slotman, Amiram Gafni, Bernard van den Berg

Bibliographic record

VenueJournal of Radiotherapy in Practice · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsMcMaster University
FundersZonMw
KeywordsContingent valuationWillingness to payMedicineValuation (finance)Travel timeValue of timeRadiation therapyEmergency medicineFamily medicineActuarial scienceSurgeryFinanceBusinessEconomics

Abstract

fetched live from OpenAlex

Abstract Aim Cancer patients spend a lot of time receiving medical care. Our study investigates patients’ preferences regarding reducing the time involved in non-palliative radiotherapy care. Methods A total of 142 Dutch patients were included in our study. Using a contingent valuation survey, we measured the proportion of patients who preferred to reduce their patients’ time, splitting it into five different categories, and, for those who did, whether and how much they were willing to pay for this to happen. Results About 50% of the patients preferred to reduce their time waiting for admission by 1 week and their travel time by half; 20 and 62% wanted to reduce their waiting time by half and their treatment time from 20 to 5 minutes, respectively; 36% preferred to be treated 7 instead of 5 days a week; and 20% of those wishing to reduce their patients’ time were willing to pay, and their mean willingness to pay (WTP) ranged from £0·32 to £18·1 per hour’s reduction of their time. Conclusion Half of the patients seem to assess their patients’ time as reasonable. The other half preferred to reduce it, but only about 20% of them were willing to pay for it to happen and their mean WTP was low.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.256
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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