“You're just one of the group when you're embedded”: report from a mixed-method investigation of the research-embedded health librarian experience
Bibliographic record
Abstract
OBJECTIVE: Embedded librarianship has received much attention in recent years. A model of embeddedness rarely discussed to date is that of research-embedded health librarians (REHLs). This study explores the characteristics of Canadian REHLs and the situations in which they are employed. METHODS: The authors employed a sequential, mixed-method design. An online survey provided descriptive statistics about REHLs' positions and work experiences. This informed a series of focus group interviews that expanded upon the survey. Through constant comparison, we conducted qualitative descriptive analysis of the interviews. RESULTS: Based on twenty-nine survey responses and four group interviews, we created a portrait of a "typical" REHL and discovered themes relevant to REHL work. REHLs may identify more strongly as researchers than as librarians, with corresponding professional needs and rewards. REHLs value "belonging" to the research team, involvement in full project lifecycles, and in-depth relationships with nonlibrarian colleagues. Despite widely expressed job satisfaction, many REHLs struggle with isolation from library and information science peers and relative lack of job security. CONCLUSIONS: REHLs differ from non-embedded health librarians, as well as from other types of embedded librarians. REHLs' work also differs from just a decade or two ago, prior to widespread Internet access to digital resources. IMPLICATIONS: Given that research-embedded librarianship appears to be a distinct and growing subset of health librarianship, libraries, master's of library and information science programs, and professional associations will need to respond to the support and education needs of REHLs or risk losing them to the health research field.
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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 | 0.029 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".