Nurse Practitioner Activities in Ontario Family Health Teams Comparing Three Different Data Sources
Bibliographic record
Abstract
BACKGROUND: Despite the increase in nurse practitioners (NPs) working in primary healthcare, little standardized data are available to understand NP activities at the system level. The Nurse Practitioner Access Reporting system (NPAR), a pilot project underway at 40 family health teams in Ontario, involves NPs recording and submitting standardized codes. The codes are intended to reflect NPs' clinical activities, using an existing physician claim system. The study compared how well data collected through NPAR reflect NPs' activities. METHODS: The mixed-methods approach was used involving NPAR data, focus groups and time and motion data. RESULTS: All data sources indicated that NPs spent the majority of their time on direct patient care. Qualitative data and time and motion data revealed gaps in NPAR data, for example, codes that fail to capture activities unique to the NP role. CONCLUSION: Analysis of NPAR, time and motion and qualitative data provided a distinctive opportunity to examine NP-reported activities and patient characteristics; however, NPAR data did not adequately describe the scope or breadth of activities of NPs practising in primary healthcare.
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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".