An Exploration of Academic Librarian Self-Efficacy in the Teaching Role: A Canadian Perspective
Bibliographic record
Abstract
The purpose of the study was to examine the impact of short professional development interventions, covering core concepts in teaching and learning, on the self- efficacy of academic librarians in the teaching role. The participants in this study were six academic librarians at a large, research-intensive university in Canada for whom instructional work was a requirement of their position. To examine librarian self-efficacy in the teaching role, the participants completed a standard self-efficacy questionnaire and a semi-structured interview prior to participating in the professional development interventions. The topics covered for professional development included learning theory, lesson planning, classroom management, and assessment, with an emphasis on formative assessment practices. After participation in the professional development, participants engaged in their regular instructional work and, six to eight weeks later, completed the standard self-efficacy questionnaire and a second semi-structured interview. The results of this study indicate librarian self-efficacy in the teaching role is a complex interplay of factors, including self-perception, faculty interactions, and institutional support. While self-efficacy is impacted by short professional development interventions on teaching, those interventions alone are not enough to develop teaching self-efficacy for academic librarians.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".