Healthcare Services Managers: What Information do They Need and Use?
Bibliographic record
Abstract
Objectives: To gain insight into the information behaviour of healthcare services managers as they draw on information while engaged in decision making unrelated to individual patient care. Objectives – The purpose of this research project was to gain insight into the information behaviour of healthcare services managers as they use information while engaged in decision-making unrelated to individual patient care. Methods – This small-scale, exploratory, multiple case study used the critical incident technique in nineteen semi-structured interviews. Responses were analyzed using ‘Framework,’ a matrix-based content analysis system. Results – This paper presents findings related to the internal information that healthcare services managers need and use. Their decisions are influenced by a wide variety of factors. They must often make decisions without all of the information they would prefer to have. Internal information and practical experience set the context for new research-based information, so they are generally considered first. Conclusions – Healthcare services managers support decisions with both facts and value-based information. These results may inform both delivery of health library services delivery and strategic health information management planning. They may also support librarians who extend their skills beyond managing library collections and teaching published information retrieval skills, to managing internal and external information, teaching information literacy, and supporting information sharing.
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 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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".