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Record W3163164566 · doi:10.1186/s12909-021-02683-y

Let’s ask the patient – composition and validation of a questionnaire for patients’ feedback to medical students

2021· article· en· W3163164566 on OpenAlexaboutno aff
Karin Björklund, Terese Stenfors, Gunnar Nilsson, Hassan Alinaghizadeh, Charlotte Leanderson

Bibliographic record

VenueBMC Medical Education · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersKarolinska Institutet
KeywordsAsk priceMedical educationPsychologyComposition (language)Educational measurementMedicineCurriculumPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Adequate communication and maintaining a patient-centered approach throughout patient encounters are important skills for medical students to develop. Feedback is often provided by clinical teachers. Patients are seldom asked to provide feedback to students that systematically addresses knowledge and skills regarding communication and patient-centeredness during an encounter. One way for patients to provide feedback to students is through a questionnaire; there is, however, a lack of such validated feedback questionnaires. This study aimed to compose and validate a feedback questionnaire for patients' feedback to medical students regarding students' ability to communicate and apply patient-centeredness in clinical practice. METHOD: This study comprises (a) composition of the questionnaire and (b) validation of the questionnaire. The composition included (1) literature review, (2) selection and composition of items and construction of an item pool, (3) test of items' content, and (4) test of the applicability of the questionnaire. The items originated from the Calgary-Cambridge Guide (Kurtz S, Silverman J, Benson J and Draper J, Acad Med 78:802-809, 2003), the 'Swedish National Patient Survey' (National Patient Survey, Primary Health Care, 2020), patient evaluation form by Braend et al. (Tidsskr Nor Laegeforen 126:2122-5, 2006), and additional developed items. The items were further developed after feedback from 65 patients, 22 students, eight clinical supervisors, and six clinical teachers. The validation process included 246 patients who provided feedback to 80 students. Qualitative content analysis and psychometric methods were used and exploratory factor analysis assessed internal validity. Cronbach's alpha was used to test the reliability of the items. RESULTS: The process resulted in the 19-item 'Patient Feedback in Clinical Practice' (PFCP) questionnaire. Construct validity revealed two dimensions: consultational approach and transfer of information. Internal consistency was high. Thematic analysis resulted in three themes: ability to capture the personal agenda of the consultation, alignment with the consultation, and constructs and characteristics. Students reported that the PFCP questionnaire provided useful feedback that could facilitate their learning in clinical practice. CONCLUSIONS: The results of this study indicate that the questionnaire is a valid, reliable, and internally consistent instrument for patients' feedback to medical students. The participants found the questionnaire to be useful for the provision of feedback in clinical practice. However, further studies are required regarding the PFCP questionnaire applicability as a feedback tool in workplace learning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.054
metaresearch head score (Gemma)0.091
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.091
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.014
GPT teacher head0.355
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreMethods

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".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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