Evaluating the Manitoba Infant Feeding Database: a Canadian infant feeding surveillance system
Bibliographic record
Abstract
OBJECTIVE: The Manitoba Infant Feeding Database (MIFD) is being piloted as a surveillance system leveraging infant vaccination visits as a point of contact to collect infant feeding data during the first year of life. The objective of this study was to assess data quality and acceptability of the MIFD as a sustainable population-based surveillance system. METHODS: Internal completeness and internal validity were measured to assess data quality. Internal completeness was defined as the number of completed data fields out of the total number of data fields. Internal validity was defined as the proportion of translation errors from one level of the system, the paper questionnaire, to the next, the electronic database. A survey assessed staff's acceptance of data collection and submission processes. RESULTS: A total of 947 records were reviewed. Data were 98.5% complete. Discrepancies were noted in 13.5% of data. The survey response rate was 78.4%. Nearly all respondents reported that the MIFD data collection tool was easy to use (96.6% agreed or strongly agreed). Whereas some challenges were identified, the majority were willing to continue with the MIFD data collection tool and process (93.1%). CONCLUSION: Results from this evaluation suggest that the MIFD data collection process worked well; however, data validation will require human resources. The MIFD approach provides a sustainable mechanism for collecting data on infant feeding for surveillance and research purposes.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".