Seven reasons why health professionals search clinical information‐retrieval technology (CIRT): toward an organizational model
Bibliographic record
Abstract
RATIONALE AND AIM: Clinical Information-Retrieval Technology (CIRT) is increasingly used, for example in accessing drug databases. However, no comprehensive framework exists to understand why health professionals search for information using CIRT. The present article aims to propose such organizational framework. BACKGROUND: Our literature review suggests six reasons, of which three refer to cognitive objectives (C1, C2, C3) and three to organizational objectives (O1, O2, O3): (C1) to answer-solve-support a clinical question-problem-decision; (C2) to fulfil an educational-research objective; (C3) to search in general or for curiosity; (O1) to share information with patients; (O2) to exchange information with other health professionals; (O3) to plan-manage-monitor tasks with other health professionals. METHODS: The case study examined the use and impact of the InfoRetriever software on handheld computers in a Canadian family practice centre. Using the Critical Incident Technique, six family doctors were interviewed on specific events. A thematic analysis assigned extracts of interviews to reasons for use. FINDINGS AND CONCLUSION: Findings illustrate the six reasons, and suggest a seventh reason that refers to a cognitive objective, namely (C4) to overcome the limits of health professional memory. These seven reasons are interpreted according to the literature on information science and organization studies, which suggest ordering reasons at three levels of stimulation of learning and knowledge: none (objective not achieved), moderate (cognitive objective achieved), and high (organizational objective achieved). This paves the way toward a new evaluation of relevance of CIRT in everyday practice (judgement based on professionals' objective achievement) using an organizational model of information-retrieving processes.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".