Information technology and hospice palliative care: social, cultural, ethical and technical implications in a rural setting
Bibliographic record
Abstract
OBJECTIVE: There is a need to better understand the specific settings in which health information technology (HIT) is used and implemented. Factors that will determine the successful implementation of HIT are context-specific and often reside not at the technical level but rather at the process and people level. This paper provides the results of a needs assessment for HIT to support hospice palliative care (HPC) delivery in rural settings. METHODS: Roundtable discussions using the nominal group technique were done to identify priority issues regarding HIT usage to support rural HPC delivery. Qualitative content analysis was then used to identify sociotechnical themes from the roundtable data. RESULTS: Twenty priority issues were identified at the roundtable session. Content analysis grouped the priority issues into one central theme and five supporting themes to form a sociotechnical framework for patient-centered care in rural settings. CONCLUSION: There are several sociotechnical themes and associated issues that need to be considered prior to implementing HIT in rural HPC settings. Proactive evaluation of these issues can enhance HIT implementation and also help to make ethical aspects of HIT design more explicit.
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 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".