Evaluating the Effectiveness of the SPIKES Model to Break Bad News – A Systematic Review
Bibliographic record
Abstract
Introduction: Breaking bad news to patients and families can be challenging for healthcare providers. The present study conducted a systematic review of the literature to determine if formal communication training using the SPIKES protocol improves learner satisfaction, knowledge, performance, or system outcomes. Method: MEDLINE, Embase, CINAHL Plus (Nursing & Allied Health Sciences), and PsycINFO Databases were searched with keywords BAD NEWS and SPIKES. Studies were required to have an intervention using the SPIKES model and an outcome that addressed at least one of the four domains of the Kirkpatrick model for evaluating training effectiveness. The Cochrane Risk of Bias Tool was used to conduct a risk of bias assessment. Due to heterogeneity in the interventions and outcomes, meta-analysis was not undertaken and instead, a narrative synthesis was used with the information provided in the tables to summarise the main findings of the included studies. Results: Of 622 studies screened, 37 publications met the inclusion criteria. Interventions ranged from the use of didactic lecture, role play with standardised patients (SPs), video use, debriefing sessions, and computer simulations. Evaluation tools ranged from pre and post intervention questionnaires, OSCE performance with rating by independent raters and SPs, and reflective essay writing. Conclusions: Our systematic review demonstrated that the SPIKES protocol is associated with improved learner satisfaction, knowledge and performance. None of the studies in our review examined system outcomes. As such, further educational development and research is needed to evaluate the impact of patient outcomes, including the optimal components and length of intervention.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".