High Impact and Highly Cited Peer-Reviewed Journal Article Publications by Canadian Occupational Therapy Authors: A Bibliometric Analysis
Bibliographic record
Abstract
A bibliometric analysis of high impact and highly cited peer-reviewed literature published between 1992 and 2016 by Canadian occupational therapy authors that were included in the Science Citation Index-Expanded (SCI-E) or Social Sciences Citation Index (SSCI) was completed. To complete the analysis, journal article titles, abstracts, author details, and keywords were searched. A second-filter identified articles where the first or corresponding author had a Canadian affiliation and occupational therapy qualification. The total number of times an article was cited since its initial publication and during 2016 in Web of Science Core Collection was recorded. A total of 919 retrieved articles met the inclusion criteria with 18 articles having 5 or more citations during 2016 alone and another 34 articles having 50 or more citations since their initial publication date. The top three journals where high impact and highly-cited articles were published were Archives of Physical Medicine and Rehabilitation, Disability and Rehabilitation, and American Journal of Occupational Therapy. The three institutions that generated the largest number of high impact and highly cited articles were McGill University, University of Toronto, and University of British Columbia. Therefore, as of 2016, Canadian occupational therapy authors published 18 high impact and 34 highly cited articles.
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 | 0.020 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.197 | 0.232 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".