MétaCan
Menu
Back to cohort
Record W7132960879

Survey of hospital library managers' attitudes toward volunteers in Ontario hospital libraries

2004· dissertation· W7132960879 on OpenAlexaboutno aff
Mary McDiarmid

Bibliographic record

VenueTSpace · 2004
Typedissertation
Language
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingWorkloadVolunteerPublic hospitalStaff managementMEDLINE
DOInot available

Abstract

fetched live from OpenAlex

The purpose of the research was to learn more about the contribution of volunteers in hospital libraries and increase our understanding of the issues and challenges related to volunteers. An anonymous, self-report 38-item questionnaire was mailed to 89 Ontario hospital library managers. Findings revealed that volunteers were used in the majority of Ontario hospital libraries. Hospital library managers hold diverse attitudes toward volunteer use. Managers viewed volunteers positively regarding helping with the workload and providing assistance with routine clerical tasks. Volunteers were viewed negatively regarding staff time needed for supervision and training of volunteers. Volunteers were more likely to be used in libraries where managers believed the library staffing was inadequate and were less likely to be used in unionized libraries. A key difference between activities performed exclusively by hospital library volunteers and those performed exclusively by public library volunteers is the complexity of tasks undertaken by hospital library volunteers.

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)0.002
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.689

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.335
Teacher spread0.297 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceSame topicNonprofit Sector and VolunteeringFrench-language works237,207