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Record W4316465255 · doi:10.15453/2168-6408.2031

Job Demands of Occupational Therapy Clinical Placements: A Descriptive Study Using the Practicum Demands Measure©

2023· article· en· W4316465255 on OpenAlexaff
Behdin Nowrouzi‐Kia, D Barker, Jill Stier, Joyce Lo

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPracticumOccupational therapyPsychologyMedical educationMental healthSittingData collectionShowerNursingMedicineApplied psychologyPhysical therapyPsychiatryEngineering

Abstract

fetched live from OpenAlex

The purpose of this study was to group and analyze the Practicum Demands Measure© (PDM) data collected over 2 academic years and create a general profile of demands across practicum settings. The data will be used to guide faculty in the most suitable placement of students requiring accommodations for a disability. The study used a secondary analysis design to analyze the 538 participant PDM data collected over the 2017/2018 and 2018/2019 academic years. Most of the sampled students were in fieldwork Level I and worked in mental health settings. The students reported physical demands, such as lifting more than 5kg (65.7%), intermittent sitting (97.6%), and keyboarding (94.6%). They also reported physical environment characteristics, such as exposure to infectious disease (44.6%) and congested working areas (27.5%). Cognitive demands included instant recall (90%) and analytical and clinical reasoning (99.8%). Practicum demands in occupational therapy were similar across other health care profession student placements, such as nursing and physical therapy. Practicum demands need to be studied more extensively to optimize students’ opportunities for success for students requiring accommodations in varied clinical settings.

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)0.027
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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 score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.002
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 head0.732
GPT teacher head0.640
Teacher spread0.092 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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