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Record W6987896751

Validation of an assessment tool for direct observation performance skills at triage (DOPS-T) for the health care professionals in the emergency department of tertiary care hospital - a work place based assessment

2018· dissertation· en· W6987896751 on OpenAlexaboutno aff

Bibliographic record

VenueeCommons - AKU (Aga Khan University) · 2018
Typedissertation
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsTriageEmergency departmentHealth careScale (ratio)Test (biology)AttendanceCertificationAcute careMEDLINEEmergency nursing
DOInot available

Abstract

fetched live from OpenAlex

Workplace based assessment is an effective way to measure the knowledge translation into clinical practice and has an important place in residency education with emphasis on skill acquisition and enhancement of learning through effective feedback. One of the goals of academic programmes in emergency medicine is to develop expertise in triage. Triage in the emergency department (ED) is the first contact point of a patient with the health care where patients are categorized as per their disease acuity which in turn can determine how fast the patient would be provided care. No instrument for workplace-based assessment of triage skills in the ED could be found. This study was aimed to develop and validate a tool for Direct Observation of Performance Skills at Triage (DOPS-T) for health care professions (HCPs) in the ED. Method The study was conducted at the emergency department of Sultan Qaboos University Hospital (SQUH) after ethics approval. SQUH is a tertiary care hospital at Muscat, Oman. Fifty HCPs (25 nurses and 25 physicians) were included in the study after informed consent. All HCP's underwent a Canadian Triage and Acuity Scale (CTAS) certification course. The change in knowledge was assessed by pre-test and post-test. After three months all FICP's were observed for their triage performance skills during the morning, evening and night shifts by two independent assessors in real-time clinical setting. Nine — point DOPS-T scale was utilized to rate the performance. SPSS version 22 and Stata version 12 were used for data analysis. Spearman correlation and interclass correlation test were used to calculate construct validity and inter-rater reliability. Effect size was calculated using Cohen's d. Learner's satisfaction and feedback were recorded. Feasibility was assessed by professionals' satisfaction and time spent in observation and providing feedback. Results Significant improvement in knowledge pertaining to triage was noticed in post-test (88.214.0) as compared to pre-test (42.2+9.0). Three hundred items were recorded using the direct observation of performance skills at triage (DOPS-T) tool for nurses and physicians. DOPS-T overall mean score on the 9-point Likert scale ±1 SD was 76.7112.44 (minimum-maximum: 57.78 - 96.67). Score was highest (8.4811.22) for 'taking the vital signs', followed by 'communication skills' (8.2110.96) while lowest score (6.32+1.58) was observed for 'reassessment done appropriately on separate items on the scale'. Summed scores were high in all the three shifts that is morning, evening and night for HCPs with more than 5 years of experience (79.12110.86 vs 74.03+8.29). Inter-rater reliability was high for assessors (ICC = 0.918 (95%0: 0.89-0.94) as well as for DOPS-T score (95%CI: 0.89-0.94). Inter-item correlation matrix showed moderate correlation. Internal consistency calculated by Cronbach's alpha was 0.911. The assessment process was rated as "very satisfied" (7-8 on a 9-point Likert scale).Mean time (±1SD) to complete the DOPS-T tool by the assessors was 15.4514.76 minutes while giving feedback took 5.77±1 .22 minutes. Conclusion The study demonstrated that recently developed DOPS-T instrument showed construct validity and reliability for direct observation of performance skills at triage for nurses and physicians. DOPS-T is feasible to be used despite of distinctive ED work hours.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.355
Teacher spread0.339 · 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 teacher head, not a consensus.

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

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Citations0
Published2018
Admission routes1
Has abstractyes

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