Nipissing District Developmental Screen: patterns of use by physicians in Ontario.
Bibliographic record
Abstract
OBJECTIVE: To determine if providing the Nipissing District Developmental Screen (NDDS) free of charge is associated with increased use of this measure and to investigate regional variations in the use of the NDDS in Ontario. DESIGN: Retrospective analysis of purchasing data from before the NDDS was available at no cost compared with analysis of the results of a brief questionnaire completed by those downloading the NDDS for free. SETTING: Ontario. PARTICIPANTS: Users of the NDDS. INTERVENTION: Provision of free on-line access to the NDDS. MAIN OUTCOME MEASURES: Patterns of purchasing or downloading of the NDDS by FPs and health care professionals (HCPs) before and after implementation of the program. RESULTS: Before the program, 91 FPs (0.9% of FPs in Ontario) purchased the NDDS, and an additional 129 FPs (1.3% of FPs in Ontario) downloaded the NDDS in the year after the start of the program. Including all other HCPs increased the estimated number of users to 504 (representing an estimated 5.0% of all FPs in Ontario). Adjusting for group practice increased the estimate to 16.5% of all FPs in Ontario who had access to the NDDS. There were no significant differences in NDDS usage by FPs between central, southwestern, and northern Ontario (P > .05). Significantly fewer FPs in eastern Ontario accessed the NDDS than FPs in other areas of the province did (P < .001). CONCLUSION: Despite measures to increase usage, only a small number of FPs access the NDDS in Ontario. However, free access to the NDDS does seem to contribute to removing barriers to screening, as indicated by a 3-fold increase in the number of FPs accessing the NDDS. Further research is required to investigate the reasons for these trends so that effective methods to increase the use of developmental screening measures in clinical practice can be implemented.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".