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Record W90941230

Using volunteers in Ontario hospital libraries: views of library managers.

2005· article· en· W90941230 on OpenAlexaffabout
Mary McDiarmid, Ethel Auster

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStaffingPopulationMedicineStaff managementFamily medicinePsychologyMedical educationNursingManagement
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Volunteers have been a resource for all types of libraries for many years. Little research has been done to describe the attitudes librarians have toward library volunteers. More specifically, the attitudes of hospital librarians toward volunteers have never been studied. OBJECTIVE: The objective was to explore and describe the extent of volunteer use and to determine library managers' attitudes toward volunteers. DESIGN, SETTING, AND PARTICIPANTS: An anonymous, self-report 38-item questionnaire was mailed to the target population of 89 hospital library managers in Ontario. Seventy-nine useable questionnaires were analyzed from an adjusted sample of 86 eligible respondents, resulting in a response rate of 92%. SPSS 11.5 was used to analyze the data. FINDINGS: The data revealed the attitudes of managers using volunteers did not differ significantly from the attitudes of managers not using volunteers. The findings showed that a majority of managers did not believe their libraries were adequately staffed with paid employees. Sufficient evidence was found of an association between a manager's belief in the adequacy of staffing in the library and the use of volunteers in the library (chi2(1, N=76)=4.11, P=0.043). Specifically, volunteers were more likely to be used by managers who did not believe their libraries were adequately staffed. The presence of a union in the library and the use of volunteers were also associated (chi2(1, N=77)=4.77, P=0.029). When unions were present in the library, volunteers were less likely to be used. IMPLICATIONS: This research has implications for hospital library managers in the management of volunteers. Volunteers should not be viewed as a quick fix or as a long-term solution for a library's understaffing problem.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.181
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.063
GPT teacher head0.262
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

Citations9
Published2005
Admission routes2
Has abstractyes

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