An Interdisciplinary Computer-based Information Tool for Palliative Severe Pain Management
Bibliographic record
Abstract
OBJECTIVES: As patient care becomes more collaborative in nature, there is a need for information technology that supports interdisciplinary practices of care. This study developed and performed usability testing of a standalone computer-based information tool to support the interdisciplinary practice of palliative severe pain management (SPM). DESIGN: A grounded theory-participatory design (GT-PD) approach was used with three distinct palliative data sources to obtain and understand user requirements for SPM practice and how a computer-based information tool could be designed to support those requirements. RESULTS: The GT-PD concepts and categories provided a rich perspective of palliative SPM and the process and information support required for different SPM tasks. A conceptual framework consisting of an ontology and a set of three problem-solving methods was developed to reconcile the requirements of different interdisciplinary team members. The conceptual framework was then implemented as a prototype computer-based information tool that has different modes of use to support both day-to-day case management and education of palliative SPM. Usability testing of the computer tool was performed, and the tool tested favorably in a laboratory setting. CONCLUSION: An interdisciplinary computer-based information tool can be developed to support the different work practices and information needs of interdisciplinary team members, but explicit requirements must be sought from all prospective users of such a tool. Qualitative methods such as the hybrid GT-PD approach used in this research are particularly helpful for articulating computer tool design requirements.
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.
How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".