A review of randomized controlled trials comparing the effectiveness of hand held computers with paper methods for data collection
Bibliographic record
Abstract
BACKGROUND: Handheld computers are increasingly favoured over paper and pencil methods to capture data in clinical research. METHODS: This study systematically identified and reviewed randomized controlled trials (RCTs) that compared the two methods for self-recording and reporting data, and where at least one of the following outcomes was assessed: data accuracy; timeliness of data capture; and adherence to protocols for data collection. RESULTS: A comprehensive key word search of NLM Gateway's database yielded 9 studies fitting the criteria for inclusion. Data extraction was performed and checked by two of the authors. None of the studies included all outcomes. The results overall, favor handheld computers over paper and pencil for data collection among study participants but the data are not uniform for the different outcomes. Handheld computers appear superior in timeliness of receipt and data handling (four of four studies) and are preferred by most subjects (three of four studies). On the other hand, only one of the trials adequately compared adherence to instructions for recording and submission of data (handheld computers were superior), and comparisons of accuracy were inconsistent between five studies. CONCLUSION: Handhelds are an effective alternative to paper and pencil modes of data collection; they are faster and were preferred by most users.
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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 | 0.039 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".