Code Status Communication Training in Postgraduate Oncology Programs: A Needs Assessment
Bibliographic record
Abstract
Background: Discussions with patients with cancer about cardiopulmonary resuscitation directives (code status) are often led by residents. This study was carried out in Canada to identify current educational practices and gaps in training for this communication skill. Methods: Canadian medical and radiation oncology residents and program directors (pds) were surveyed about teaching practices, satisfaction with current education, and barriers to teaching code status discussion skills. Relative frequencies of categorical and ordinal responses were calculated. Results: Between November 2016 and February 2017, 95 (58.6%) of 162 residents and 17 (63%) of 27 pds completed surveys. Only 54.1% and 48.3% of medical and radiation oncology residents, respectively, had received any code status communication training before entering an oncology program. While 41% of residents expected to receive formal teaching on this topic during residency, 47.1% of pds endorsed inclusion of this topic in curricula. Only 20% of residents reported receiving formal evaluation of this skill while 41.2% of pds indicated that evaluations are provided. The importance of this communication skill in oncology was strongly supported. Among residents, 88% desired more training, and 82.3% of pds identified the need for new educational resources. Lack of time, resources, and evaluation tools were among the most commonly identified barriers to teaching. Conclusions: Oncology residency pds and trainees feel that code status communication is important, but teaching and evaluation of this skill are limited. Barriers to teaching and skill-building have been identified. Further work is underway to develop novel educational resources for code status communication training.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".