Evidence map›Paper›PMID 35983576›Full record

ArticlePatient related outcome measures2022

Characteristics of Exceptionally Good Doctors: A Protocol for a Cross-Sectional Survey of Adults.

Christoph Schnelle, Mark A Jones

Abstract read
In one paragraph

Article in Patient related outcome measures, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Christoph SchnelleInstitute of Evidence-Based Healthcare, Bond University, Robina, Queensland, Australia.ORCID 0000-0001-8538-1487
Mark A JonesInstitute of Evidence-Based Healthcare, Bond University, Robina, Queensland, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Doctors constitute a significant proportion of a very large number of medical interactions. They are known to vary in the quality of their work, with some having an exceptionally beneficial effect on patients' physical health. In a qualitative study, we interviewed medical doctors on their opinions and experiences of exceptionally good doctors. Their responses and the results from previous research are used as a basis for this proposed cross-sectional survey directed to members of the public on their encounters with exceptionally good doctors. The primary aim of this cross-sectional study is to describe the characteristics of exceptional doctors as reported by a large representative sample of adult patients. Methods and Analysis: A mixed qualitative and quantitative anonymous cross-sectional survey of 500 Amazon Mechanical Turk (MTurk) respondents, who have met one or more exceptionally good doctors in their life, will be conducted. Information requested will include reasons for nominating a particular doctor; experience of how that doctor differs from other and average doctors; and 34 5-point Likert scale questions on the characteristics of that doctor and the same Likert questions for the average doctor. An opportunity to report their experience in free-text form will be provided. Sample size will be sufficient to obtain a margin of error of 4%. The authors will provide descriptive statistics, including graphs of the Likert scale question responses; conduct factor analysis for internal validity; investigate satisficing and logical inconsistencies; and explore whether there are multiple types of exceptionally good doctors. Discussion: Previous surveys of patients' perceptions of doctors exist though none have focused on exceptionally good doctors. The expected results will include a list of characteristics that are important to patients in determining exceptionally good doctors.

Indexed as

cross-sectional surveydoctors’ performancegood doctorsmedical practicepatients’ opinion

Identifiers

PMID35983576
PMCPMC9381005

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.