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Record W6931890625 · doi:10.5287/ora-pvzr2mbg5

Development and validation of an approach for self-assessment of communication skills in clinical dentistry: a mixed methods evaluation in Syria

2024· dissertation· en· W6931890625 on OpenAlexaboutno aff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2024
Typedissertation
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsGeneralizability theoryThematic analysisReliability (semiconductor)Cronbach's alphaQualitative researchCommunication skillsExploratory factor analysisQualitative propertyMultimethodology

Abstract

fetched live from OpenAlex

Communication skills are universally recognised as a core competency in clinical education, yet in many Arab countries, including Syria, these skills are not formally integrated into the curriculum. This study aimed to develop and validate a self-assessment approach for communication skills, incorporating patient assessments for corrective feedback, at Damascus University Faculty of Dental Medicine, Syria. The study investigated this assessment approach's reliability, validity, and educational impact. Using the well-established Calgary Cambridge Guide, an assessment instrument was developed, translated and cross-culturally validated for the Syrian Arabic context. The assessment approach comprised six consultation sessions with six patients in clinical settings. After each consultation, students and patients completed an assessment form. Fifty-four final-year dental students completed all six sessions. A sequential mixed methods design was employed, beginning with a quantitative phase that included generalizability (G) and decision studies to assess reliability and exploratory factor analysis (EFA) to evaluate structural validity. In the qualitative phase, semi-structured interviews were conducted with a purposive subsample (n=12) from the quantitative strand. The thematic analysis explored the educational impact and factors influencing self-assessment. Findings showed a G-coefficient of 0.93 for self-assessment and 0.66 for patient assessment. Student and rater effects are conflated in self-assessment G-study, so the coefficient needs to be interpreted cautiously. EFA indicated a one-factor solution explaining 52.1% of the variance. There was a significant improvement in patient assessments over time, with a large effect size (P<0.001, Partial η²=0.184). Qualitative results supported these findings, revealing increased self-awareness and self-control due to self-assessment. Subjective self-assessment practices, patient-dentist dynamics, and self-reflection all influenced self-assessment scoring. Focusing on a context that lacks a formal communication skills curriculum, this rigorous mixed methods research makes an original and substantive contribution to the field. One original contribution of this study is the in-depth exploration of meanings and intentions underlying self-assessment results that most studies in the field overlook. Although the findings should be generalised cautiously, this research provides valuable recommendations for students, clinicians, and clinical educators on optimising self-assessment practice and using its data to facilitate communication performance improvement.

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)0.098
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0980.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.101
GPT teacher head0.479
Teacher spread0.378 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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