Satellite stories: capturing professional experiences of academic health sciences librarians working in delocalized health sciences programs
Bibliographic record
Abstract
OBJECTIVE: Health sciences training programs have progressively expanded onto satellite campuses, allowing students the opportunity to learn in communities away from an academic institution's main campus. This expansion has encouraged a new role for librarians to assume, in that a subset of health sciences librarians identify as "satellite librarians" who are permanently located at a distance from the main campus. Due to the unique nature of this role and lack of existing data on the topic, the authors investigated the experiences and perceptions of this unique group of information professionals. METHODS: An electronic survey was distributed to health sciences librarians via two prominent North American email discussion lists. Questions addressed the librarians' demographics, feelings of social inclusion, technological support, autonomy, professional support, and more. RESULTS: Eighteen surveys were analyzed. While several respondents stated that they had positive working relationships with colleagues, many cited issues with technology, scheduling, and lack of consideration as barriers to feeling socially included at both the parent and local campuses. Social inclusion, policy creation, and collection management issues were subject to their unique situations and their colleagues' perceptions of their roles as satellite librarians. CONCLUSIONS: The results from this survey suggest that the role of the academic health sciences librarian at the satellite campus needs to be clearly communicated and defined. This, in turn, will enhance the experience for the librarian and provide better service to the client.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".