Learning while doing: program evaluation of the Medical Library Association Systematic Review Project
Bibliographic record
Abstract
OBJECTIVES: The Medical Library Association (MLA) Systematic Review Project aims to conduct systematic reviews to identify the state of knowledge and research gaps for fifteen top-ranked questions in the profession. In 2013, fifteen volunteer-driven teams were recruited to conduct the systematic reviews. The authors investigated the experiences of participants in this large-scale, volunteer-driven approach to answering priority research questions and fostering professional growth among health sciences librarians. METHODS: A program evaluation was conducted by inviting MLA Systematic Review Project team members to complete an eleven-item online survey. Multiple-choice and short-answer questions elicited experiences about outputs, successes and challenges, lessons learned, and future directions. Participants were recruited by email, and responses were collected over a two-week period beginning at the end of January 2016. RESULTS: Eighty (8 team leaders, 72 team members) of 198 potential respondents completed the survey. Eighty-four percent of respondents indicated that the MLA Systematic Review Project should be repeated in the future and were interested in participating in another systematic review. Team outputs included journal articles, conference presentations or posters, and sharing via social media. Thematic analysis of the short-answer questions yielded five broad themes: learning and experience, interpersonal (networking), teamwork, outcomes, and barriers. DISCUSSION: A large-scale, volunteer-driven approach to performing systematic reviews shows promise as a model for answering key questions in the profession and demonstrates the value of experiential learning for acquiring synthesis review skills and knowledge. Our project evaluation provides recommendations to optimize this approach.
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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 | 0.434 | 0.441 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier 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".