How does Canada stack up? A bibliometric analysis of the primaryhealthcare electronic medical record literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Major initiatives are underway in Canada which are designed to increase electronic medical record (EMR) implementation and maximise its use in primary health care. These developments need to be supported by sufficient evidence from the literature, particularly relevant research conducted in the Canadian context. OBJECTIVES: This study sought to quantify this lack of research by: (1) identifying and describing the primary health care EMR literature; and (2) comparing the Canadian and international primary healthcare EMR literature on the basis of content and publication levels. METHODS: Seven bibliographic databases were searched using primary health care and EMR keywords. Publication abstracts were reviewed and categorised. First author affiliation was used to identify country of origin. Proportions of Canadian- and non-Canadian-authored publications were compared using Fisher's exact test. For countries having 10 or more primary healthcare EMR publications, publications per 10 000 researchers were calculated. RESULTS: After exclusions, 750 publications were identified. More than one-third used primary healthcare EMRs as a study data source. Twenty-two (3%) were Canadian-authored. There were significantly different publication levels in three categories between Canadian- and non-Canadian-authored publications. Based on publications per researchers, the Netherlands ranked first, while Canada ranked eighth of nine countries with 10 or more publications. CONCLUSIONS: A relatively small body of literature focused on EMRs in primary health care exists; publications by Canadian authors were low. This study highlights the need to develop a strong evidence base to support the effective implementation and use of EMRs in Canadian primary health care.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.036 | 0.207 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it