Evaluation of a Data Collection Tool (TELE <i>form</i> <sup>®</sup> ) for Palliative Care Research
Bibliographic record
Abstract
INTRODUCTION: The Alberta Cancer Board Palliative Care Research Initiative (ACBPCRI) encourages province-wide collaboration on palliative care research projects. Because of geographic differences in information system infrastructure, it is necessary to evaluate and adopt a data collection tool that will span the variability in system hardware and software. We assessed TELEform (Cardiff Sofware Inc., Vista CA), an optical recognition-based technology that scans data collection paper forms and exports data to a computer database. We examined work place suitability, data quality, and effective resource utilization (time and cost) during the data collection tool evaluation. METHODS: Two hospices and two hospitals from the cities of Edmonton and Calgary participated in the revised Edmonton Staging System (rESS) project that used TELEform as its data collection tool. The evaluation was conducted over a period of 7 months. Data source such as e-mail and summary notes collected primarily through meetings and discussions with management, caregivers, researchers, and clerical staff was used to assess work practice and resource utilization. Descriptive statistics was employed to examine data quality and resource utilization. RESULTS: One hundred seventy eight patients were recruited during the 7-month trial. The costs and time involved in staff training, logistic support, and equipment startup were found to be reasonable. Data error and missing data were 0.4% and 0.6%, respectively. We initially encountered several problems with TELEform. The optical recognition tool could not easily pick up handwritten data. Furthermore, it was unforgiving in the sense that an error was not correctable by an eraser on the paper form. Data collectors found TELEform usage to be easy and simple because it retained the familiarity of paper-based recording. CONCLUSION: It is important to develop an information infrastructure to support research project data collection for different health settings across health regions. The TELEform based on optical recognition was able to respond to the need for current information processing. We believe that TELEform is a useful tool in terms of work practice, data quality, and resource utilization.
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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 | 0.238 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".