An Examination of Health Information Professionals’ Discourse Surrounding Knowledge Synthesis
Bibliographic record
Abstract
Introduction: Knowledge Synthesis (KS) is an umbrella term that encompasses a family of research methods that aim to draw insights from existing bodies of research literature through established processes of review and analysis. Providing support services for KS is part of many health libraries’ day-to-day business and has been for several years. Methods: This article presents the results of a content analysis conducted to gain insight into our collective relationship with KS using journal articles and conference abstracts associated with the Canadian Health Libraries Association and the Medical Library Association. The study is framed in terms of three broad questions: What do health information professionals talk about when they discuss knowledge synthesis? How do they talk about it? And who is doing the talking? Descriptive codes and attribute codes were applied to the texts. Descriptive codes were grouped and regrouped to create sub-themes and overall themes in a bottom-up fashion. Results: Three broad themes are evident in the texts: the Case for KS Work, Everyday Realities of KS, and Pushing Back. KS discourse in the venues examined is currently dominated by the voices of health information professionals working in academic libraries, though this was not always the case. Commentary: I suggest that our collective relationship with KS work has been changing, and health information professionals are starting to become more comfortable with setting boundaries around KS work.I also suggest that an apparent shift in voice towards academic health information professionals and a general increase in the KS content included in these venues over time could be attributed to 1) the widespread adoption of evidence-based practice among our clientele, and 2) increased emphasis on research impact assessment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.181 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.021 | 0.001 |
| Scholarly communication | 0.001 | 0.019 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".