Developing a code of practice for literature searching in health sciences: a project description
Bibliographic record
Abstract
Introduction: Libraries have provided mediated search services for more than forty years without a practice standard to guide the execution of searches, training of searchers, or evaluation of search performance. A pan-Canadian group of librarians completed a study of the literature on mediated search practices from 2014-2017 as a first step in addressing this deficit. Methods: We used a three-phase, six-part content analysis process to examine and analyze published guidance on literature searching. Card sorting, Delphi methods, and an online questionnaire were then used to validate our findings and build a code of practice. Results: Our code of practice for mediated searching lists eighty-five search tasks arranged in performance order, within five progressive levels of search complexity. A glossary of 150 search terms supports the code of practice. Discussion: The research literature on mediated search methods is sparse and fragmented, lacking currency and a shared vocabulary. A code of practice for mediated searching will provide clarity in terminology, approach, and methods. This code of practice will provide a unified and convenient reference for training a new hire, upholding standards of search service delivery, or educating the next wave of health library professionals.
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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 | 0.168 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".