Problems Experienced by Health Care Professionals with Do not Attempt Resuscitation (DNAR) Orders – A Qualitative Study
Bibliographic record
Abstract
A 'Do Not Attempt Resuscitation' (DNAR) order is one of the most important yet difficult medical decisions. Despite the recent European guidelines, health care professionals (HCPs) in general perceive challenges in making a DNAR order. We aimed to evaluate the types of problems related to DNAR order making. A link to a web-based multiple-choice questionnaire including open-ended questions was sent by e-mail to all physicians and nurses working in the Tampere University Hospital special responsibility area covering a catchment area of 900,000 Finns. The questionnaire covered issues on DNAR order making, its meaning and documentation. Here we report the analysis of the open-ended questions, examined based on the Ottawa Decision Support Framework with expanded individual decisional needs categories. Qualitative data describing respondents' opinions (N=648) regarding problems related to DNAR order decision making were analysed using Atlas.ti 23.12 software. In total, 599 statements (phrases) dealing with inadequate advice, information, emotional support, and instrumental help were identified. Our results show that HCPs experience lack of support in DNAR decision making on multiple levels. Digital decision-making support integrated into electronic patient records (EPR) to assure timely and clearly visible DNAR orders could be beneficial.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".