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Record W6884628101 · doi:10.11575/prism/34722

Tracking the Impact of Changes to a Provincial Library Service Model: The Results of Two Satisfaction Surveys

2015· other· en· W6884628101 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsService (business)Resource (disambiguation)Tracking (education)Descriptive statisticsHealth servicesInformation resourceSurvey research

Abstract

fetched live from OpenAlex

Abstract: Introduction: Alberta Health Services (AHS) was created in 2009, merging 12 former health regions and three provincial health authorities. Library services that had previously operated independently across the province were amalgamated into a single provincial entity, Knowledge Resource Service (KRS). A survey of library services was conducted in 2011. Subsequent to that survey, the provincial library service underwent major changes, which culminated in the launch of a provincial library website in August 2013. Another survey was conducted in 2014 to determine the impact of these changes. Methods: AHS staff and physicians were surveyed in 2011 and 2014 using an electronic survey tool. The survey results were analyzed using descriptive statistics and the results reported as percentages. Results: This paper addresses the questions that are comparable between the two surveys. There were 1195 responses to the 2011 survey and 721 to the 2014 survey. Respondents in 2014 had less difficulty accessing the library website. Additionally, more respondents reported that using library resources prevented the occurrence of adverse events, from 13% in 2011 to 36% in 2014. Discussion: The drop in respondents reporting difficulties accessing information via the library website suggests that the new service model is effective in removing access barriers, enhancing the effectiveness of information resources for AHS staff and that the easier to find resources are helping to prevent adverse events.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.573
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.253
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreOther

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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