Clinical ethics consultations: a scoping review of reported outcomes
Bibliographic record
Abstract
BACKGROUND: Clinical ethics consultations (CEC) can be complex interventions, involving multiple methods, stakeholders, and competing ethical values. Despite longstanding calls for rigorous evaluation in the field, progress has been limited. The Medical Research Council (MRC) proposed guidelines for evaluating the effectiveness of complex interventions. The evaluation of CEC may benefit from application of the MRC framework to advance the transparency and methodological rigor of this field. A first step is to understand the outcomes measured in evaluations of CEC in healthcare settings. OBJECTIVE: The primary objective of this review was to identify and map the outcomes reported in primary studies of CEC. The secondary objective was to provide a comprehensive overview of CEC structures, processes, and roles to enhance understanding and to inform standardization. METHODS: We searched electronic databases to identify primary studies of CEC involving patients, substitute decision-makers and/or family members, clinicians, healthcare staff and leaders. Outcomes were mapped across five conceptual domains as identified a priori based on our clinical ethics experience and preliminary literature searches and revised based on our emerging interpretation of the data. These domains included personal factors, process factors, clinical factors, quality, and resource factors. RESULTS: Forty-eight studies were included in the review. Studies were highly heterogeneous and varied considerably regarding format and process of ethical intervention, credentials of interventionist, population of study, outcomes reported, and measures employed. In addition, few studies used validated measurement tools. The top three outcome domains that studies reported on were quality (n = 31), process factors (n = 23), and clinical factors (n = 19). The majority of studies examined multiple outcome domains. All five outcome domains were multidimensional and included a variety of subthemes. CONCLUSIONS: This scoping review represents the initial phase of mapping the outcomes reported in primary studies of CEC and identifying gaps in the evidence. The confirmed lack of standardization represents a hindrance to the provision of high quality intervention and CEC scientific progress. Insights gained can inform the development of a core outcome set to standardize outcome measures in CEC evaluation research and enable scientifically rigorous efficacy trials of CEC.
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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 | 0.103 | 0.347 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.071 | 0.063 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".