Testing the Satisfaction and Feasibility of a Computer-Based Teaching Module in the Neonatal Intensive Care Unit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it