Determinants of a high-quality consultation in medical communications: a systematic review of qualitative and quantitative evidence
Bibliographic record
Abstract
Purpose The medical consultation is one of the requirements in diseases diagnosis and patient treatment. In addition, a high-quality consultation is a fundamental demand of patients, and it is one of the rights of every patient. The purpose of this paper is to identify factors affecting the high-quality consultation in medical communications. Design/methodology/approach The following electronic databases were searched: MEDLINE (via PubMed), Web of Science, Cochrane, EMBASE, Scopus and ProQuest until December 2018. In addition, the authors searched Google Scholar. Qualitative and quantitative studies were assessed using the Critical Appraisal Skills Programme, Qualitative Checklist and the Center for Evidence-Based Management appraisal checklist, respectively. A stepwise approach was conducted for data synthesis. Findings Of 3,826 identified studies, 29 met the full inclusion criteria. Overall, after quality assessment of studies, 25 studies were included. The studies were conducted in the USA (n=6), the UK (n=6), the Netherlands (n=4), Canada (n=2), Belgium (n=2), Poland (n=2), Germany (n=1), Iran (n=1), Finland (n=1), Austria (n=1), Qatar (n=1), Denmark (n=1) and China (n=1), and five studies were excluded. Data synthesis showed that high-quality consultation consisted of three main categories: structural (4 main themes with 26 sub-themes), process (2 main themes with 33 sub-themes) and outcome (3 main themes with 12 sub-themes) quality. Originality/value Using the indicators of consultation quality improvement can develop physicians’ clinical competence and skills. Decision makers can use them to monitor and evaluate physicians’ performance. A high-quality consultation can be useful in social prescribing that helps patients to manage their disease.
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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 | 0.087 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".