Practices and Barriers of Inter-Professional Collaboration with Teacher-librarians and Teachers: A Content Analysis
Bibliographic record
Abstract
Thematic content analysis was used to identify the practices, and barriers of inter-professional collaboration among teacher-librarians and classroom teachers. Twelve structured research were identified and information regarding the themes was extracted verbatim. The data collected on practices were analysed to see how they correlated with to Loertscher and Koechlin’s (2016) conceptual framework for collaboration and co-teaching among teacher-librarians and classroom teachers. The barriers were examined and analyzed to gain an understanding of suitable recommendations to alleviate same. It was discovered that the involvement of school administration in the collaborative process; the presence of curriculum integration; and the provision of a common space for teaching were important practices for successful inter-professional collaboration. On the other hand, the lack of understanding of the role of both professionals and the workload of teachers were identified as some of the barriers. It was recommended that these can be alleviated if each professional spends time to know about each other’s job description and learn how to integrate the library seamlessly into the curriculum so that the workload of the teacher is not increased.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".