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Record W2568224845 · doi:10.1177/0008417416660990

Beyond research literature: Occupational therapists’ perspectives on and uses of “evidence” in everyday practice

2016· article· en· W2568224845 on OpenAlexvenueno aff
Deborah A. Dougherty, Susan Toth‐Cohen, George Tomlin

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyEvidence-based practicePsychologyPerspective (graphical)DocumentationEvidence-based medicineIntervention (counseling)Systematic reviewQualitative researchMedical educationApplied psychologyMEDLINEMedicineAlternative medicineSociologyPsychiatrySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Recent literature in evidence-based practice indicates that evidence is defined differently from practice and research perspectives. However, few published works address therapists' perspectives of the nature and use of evidence in everyday practice. PURPOSE: This study describes the definition, types, and use of evidence from the perspective of six school-based occupational therapists. METHOD: Data were collected through focus groups and participant-submitted documentation and analyzed using qualitative and quantitative content analysis. FINDINGS: Two categories of evidence emerged: internalized evidence and evidence gathered during the intervention process. Clinical reasoning, identified as a key skill in evidence-based practice, supported the synthesis of therapist internalized evidence with "in-the-moment" evidence gathered from activity, contextual, and occupational analyses of the client. IMPLICATIONS: The findings support current literature that has suggested expanding the definition of evidence (i.e., more than research findings alone). Further investigation of evidence building in practice may help in constructing a more inclusive professional culture of evidence-based practice.

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)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.615
GPT teacher head0.598
Teacher spread0.018 · 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.

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".

Quick stats

Citations38
Published2016
Admission routes1
Has abstractyes

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