Significant Value Found in Mentoring Programs for Novice Tenure-Track Academic Librarians
Bibliographic record
Abstract
A Review of: Goodsett, M., & Walsh, A. (2015). Building a strong foundation: Mentoring programs for novice tenure-track librarians in academic libraries. College & Research Libraries, 76(7), 914-933. http://dx.doi.org/10.5860/crl.76.7.914 Objective – To examine the effectiveness of mentoring programs for novice tenure-track academic librarians, and to identify critical elements that define a successful mentoring program in various academic library settings. Design – Survey questionnaire with a voluntary phone interview. Setting – Academic libraries in the United States of America. Subjects – 283 librarians participated in a survey questionnaire. Researchers conducted additional interviews with 6 out of the 12 librarians who had volunteered on the survey questionnaire and who met the inclusion criteria. Methods – Researchers recruited participants through two professional e-mail lists: the Information Literacy Instruction Discussion List (ILI-L) listserv and the American Library Association’s New Members Round Table (NMRT) listserv. Interested participants completed a secured online survey that was hosted using SurveyMonkey. The researchers then coded and analyzed the collected survey data using the same software. At the end of the online survey, participants were given the opportunity to volunteer for an additional interview. Potential interviewees were selected if mentoring programs were available for tenure- track librarians at their institutions. Once selected, researchers contacted potential interviewees and conducted interviews. The interviews were transcribed, the data anonymized, and original recordings deleted. Researchers coded the anonymized interview data to identify common themes. Main Results – Researchers identified six themes from the survey data and interview transcripts: traits of an effective mentor; configuration of mentoring programs; elements of effective mentoring programs; mentoring partnerships within or beyond the library; role and training of mentors and mentees; and the mentor/mentee relationship. Overall, the survey and interview data suggest that mentoring programs provide valuable assistance with professional tenure-related activities, and facilitate less-tangible effects such as an increased understanding of an institution’s culture and an improvement of communication and time-management skills. The data also provides insight into effective program elements and areas for improvement. Conclusion – This study suggests that there is significant value in implementing a mentoring program. The results from this study can be used by academic libraries that are considering implementation of a mentoring program or improving an existing one.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".