Bibliographic record
Abstract
This issue also contains nine very interesting and diverse articles.They range from control charts as an early-warning system for workplace health outcomes to salient components in supported employment programs from the perspectives of employment specialists and clients to the lack of genotoxicity in medical oncology nurses handling antineoplastic drugs.We especially enjoy advancing the evidence literature on computing.There are two articles related to computing in this issue.One article investigates the effects of user friendly keyboard slope modifications on wrist postures; and the other article investigates upper extremity musculoskeletal discomfort among notebook computer users.Concerns related to low back are addressed in two articles.Poitras, Durand, Côté and Tousignant conducted a qualitative study of the barriers and facilitators to the use of low-back pain guidelines by occupational therapists.Researchers Leggett and Cowley provide an interesting study of manual handling risks associated with the care, treatment, and transportation of bariatric (severely obese) patients and clients in Australia.Kuruganti, Murphy, and Dickinson share a preliminary investigation of upper limb muscle activity during simulated Canadian forest harvesting operations.Harr, Dunn, and Price advance the importance of household task participation with their article on the effect of household task participation in the contexts of home, community and work by youth with multiple disabilities.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.179 | 0.089 |
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".