Knowledge gaps, attitudes, and practices regarding end-of-life medical care among physicians in an academic medical center
Bibliographic record
Abstract
<b>Introduction:</b> End-of-life medical care (ELMC) plans and do-not-resuscitate (DNR) decision-making are usually affected by multiple factors compared to other medical care decisions.<b> </b>ELMC and DNR policy implementation are still diversified and heterogeneous, especially in Saudi Arabia, because policymakers have adopted no guidelines. Thus, this study investigated physicians’ knowledge, attitude, and practice regarding ELMC and DNR.<br /> <b>Methods:</b> A cross-sectional study design was adopted. Three hundred physicians working at King Fahad Hospital of the University, Khobar, Saudi Arabia, were randomly selected and administered an anonymous self-administered questionnaire using the Likert scale. Data analysis was carried out using SPSS 23.0.<br /> <b>Results: </b>Of 300 distributed questionnaires, 264 (88%) were completed and analysed. Knowledge gaps and negative attitudes were observed, a quarter of the participants were opposed to issuing a DNR order, and 29.0% considered DNR as equal to euthanasia as they practice. The participants’ patient age and religious factors were the most critical factors in the ELMC plan and DNR decision. The physician’s level of acceptance regarding a set of ELMC interventions and DNR decisions showed heterogenicity and uncertainty among participants.<br /> <b>Conclusions:</b> The ELMC plan and DNR decision-making should be appropriately addressed in the medical residents’ training programs to bridge the knowledge gap and the physicians’ negative attitudes during their practice. Additionally, there is a need to update and unify the DNR policies at the national level, considering the patient’s right to be informed and involved actively during the decision process making. Finally, more prospective research is needed for the global standardization of ELMC.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".