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Record W2152958049 · doi:10.1177/0899764003260961

Valuing Volunteers: An Economic Evaluation of the Net Benefits of Hospital Volunteers

2004· article· en· W2152958049 on OpenAlexaffabout
Femida Handy, S. Narasimhan

Bibliographic record

VenueNonprofit and Voluntary Sector Quarterly · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsYork University
Fundersnot available
KeywordsVolunteerLiberian dollarCommunity hospitalNonmarket forcesBusinessInvestment (military)Value (mathematics)Cost–benefit analysisMedicineFamily medicineNursingFinanceEconomicsPolitical science

Abstract

fetched live from OpenAlex

The use of volunteers in hospitals has been an age-old practice. This nonmarket community involvement is a distinctive aspect of North American life. Hospitals may be attracted to increase the use of volunteers, both to provide increased quality of care and to contain costs. Hospitals rely on the use of professional administrators to use the donated time of volunteers efficiently. This study examines the benefits and costs of volunteer programs and derives an estimate of the net value of volunteer programs that accrue to the hospitals and volunteers. In particular, the costs and benefits to hospitals are detailed. Using 31 hospitals in and around Toronto and surveying hospital volunteer administrators, hospital clinical staff members, and volunteers themselves, a striking pay-off for hospitals was found: an average of $6.84 in value from volunteers for every dollar spent—a return on investment of 684%. Civic and community participation is indeed valuable.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.045
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.283
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations197
Published2004
Admission routes2
Has abstractyes

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