Needs Assessment on Continuing Professional Development for Virtual Care: Final Report
Bibliographic record
Abstract
This is the report of a needs assessment on continuing professional development for virtual care conducted between November 2021 and March 2022 by the Office of Professional and Educational Development (OPED)), Faculty of Medicine, Memorial University. The Newfoundland and Labrador Centre for Health Information (NLCHI) provided funding in support of this project. The purpose of this needs assessment was to explore the education and training needs of physicians, health care providers and patients on virtual care. \nThe needs assessment study was undertaken by collecting information and data using the following methods: \n• a rapid literature review comprised of various types of research publications such as conference proceeding and peer-reviewed journal papers published since March 2020; \n• an environmental scan of key grey literature including governmental and non-governmental professional association reports and guidelines from Canada; \n• key informant interviews with N=7 informants from national organizations and/or institutions with expertise in virtual care delivery; \n• an online survey-questionnaire with N=1013 healthcare providers in the province of Newfoundland and Labrador; \n• a patient focus group with N=5 patient partner representatives from the Patient Advisory Council associated with NL Support.
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 | 0.047 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".