P.105 Smartphone and mobile app use among Canadian Neurosurgery residents and fellows
Bibliographic record
Abstract
Background: Communicating with senior neurosurgical colleagues during residency necessitates a reliable and versatile smartphone. Smartphones and their apps are commonplace. They enhance communication with colleagues, provide the ability to access patient information and results, and allow access to medical reference applications. Patient data safety and compliance with the Personal Health Information Protection Act (PHIPA, 2004) in Canada remain a public concern that can significantly impact the way in which mobile smartphones are utilized by resident physicians Methods: Through the Canadian Neurosurgery Research Collaborative (CNRC), an online survey characterizing smartphone ownership and utilization of apps among Canadian neurosurgery residents and fellows was completed in April 2016. Results: Our study had a 47% response rate (80 surveys completed out of 171 eligible residents and fellows). Smartphone ownership was almost universal with a high rate of app utilization for learning and facilitating the care of patients. Utilization of smartphones to communicate and transfer urgent imaging with senior colleagues was common. Conclusions: Smartphone and app utilization is an essential part of neurosurgery resident workflow. In this study we characterize the smartphone and app usage within a specialized cohort of residents and suggest potential solutions to facilitate greater PHIPA adherence
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".