Testing the Satisfaction and Feasibility of a Computer-Based Teaching Module in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
PURPOSE: To examine the satisfaction with and feasibility of a computer-based teaching module to teach healthcare professionals how to use and apply the Premature Infant Pain Profile (PIPP) to clinical scenarios. SUBJECTS: Sixty-eight healthcare professionals who were employed in the neonatal intensive care unit (NICU) on a full-time or part-time basis and had received an educational session regarding the PIPP. DESIGN AND METHODS: A pilot study using an exploratory descriptive design was used to answer: (1) How satisfied are healthcare professionals with the computer-based teaching module? and (2) What is the feasibility of a computer-based teaching module in the clinical setting? Satisfaction was measured using an investigator-developed 5-point Likert scale. Feasibility was measured in terms of time to complete the module, satisfaction with instructions and ability to navigate through the module, acceptability of the module as a teaching method, and format with the computer-based module. PRINCIPAL RESULTS: Ninety percent of those sampled were very satisfied with the computer-based teaching method. Use of video and audio clips and photographs enhanced the learning process. Healthcare professionals identified the computer-based teaching method as an effective way of learning about the PIPP and thought that it was feasible to use within the clinical setting. CONCLUSIONS: Computer-based teaching is a feasible method for educating NICU healthcare professionals about the PIPP. Additional research is required to examine the effectiveness of this teaching method on relevant patient outcomes such as pain management.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".