Evidence-based administrative decision making and the Ontario hospital CEO: information needs, seeking behaviour, and access to sources
Bibliographic record
Abstract
Introduction -The hospital librarian requires an understanding of the information needs, information-seeking process, and use of information resources by a hospital's chief executive officer (CEO) so that the librarian may support, promote, and foster evidence-based decision making (EBDM) at the executive level.This research aimed to identify various reasons hospital CEOs seek information and uncover their feelings and thoughts about the process.Method -In this study, funded by the Canadian Health Libraries Association / Association des bibliothèques de la santé du Canada (CHLA / ABSC), Ontario hospital CEOs were interviewed by telephone in the summer of 2006.Findings -Barriers to EBDM as described by the CEOs included a lack of on-demand information and limited time for the information-seeking process.The CEOs preferences regarding the content and delivery method of needed information and the CEOs specific information needs and wants are described in this paper.Ontario CEOs do not perceive the hospital library as a first source that they turn to for EBDM.Of the 27 CEOs interviewed who directly use a library (onor off-site), 37% did not know the librarian's name.CEOs were asked whether they believed a hospital library would exist 5-10 years from now and to forecast the future for library services.The CEOs envision library services as shared or joint services, or virtual, or both.Conclusion -We have concluded from the findings that the hospital librarian who has not already communicated their expertise and demonstrated their ability to link the strategic goals of the hospital to available evidence-based resources will not be around in 10-15 years.
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How this classification was reachedexpand
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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".