Transforming a Library Service within a Provincial Healthcare Organization: Forging a New Path
Bibliographic record
Abstract
Introduction: Prior to 2011, libraries within Alberta Health Services (AHS) operated using a variety of self-determining service models across 19 locations. Evaluation of library services demonstrated significant gaps in service delivery and access to resources, cost inefficiencies and variation in library service standards across the province. National and international trends reflected ongoing library closures and challenges to demonstrate library contributions to organizational goals and improvements in health information literacy. Description: In January 2011, all AHS library services were aligned under the Knowledge Management Department to capitalize on the natural fit between libraries as conduits to evidence and knowledge management practices that support the use of evidence in practice. The mandate was to develop enterprise-wide library resources and services to support clinical decision-making and quality patient care under the umbrella of the Knowledge Resource Service (KRS). The Business Case for KRS Optimization guided this initiative. Outcome: KRS is now a focal point for access to, and expertise in, healthcare information resources and services. Organization-wide evaluations conducted in 2011 and 2014 show increased user satisfaction, while utilization analytics reflect continued growth. Discussion: The KRS Optimization Initiative was a proactive, internally driven effort to extend library services and resources beyond the traditional library space, streamline ‘back-office’ functions and allow staff to contribute to organizational initiatives. The path has been winding yet lessons learnt include the value of dedicated staff, teamwork, and maintaining a focus on improving service for all AHS staff and clinicians.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Case report | low |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.035 | 0.023 |
| Scholarly communication | 0.029 | 0.014 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".