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Observational movement analysis and clinical reasoning about the sit to stand movement in a person with stroke: Exploring perspectives of student and expert neurological physiotherapists

2024· preprint· en· W4391402389 on OpenAlexaff
Chelsea Louise Chua, Robert Chong, Sarah Hill, Shireen Ng, Jasmine Chan, Nada Hassan, Kara K. Patterson, Julie Vaughan‐Graham

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsObservational studyPsychologyAcquired brain injuryNeurorehabilitationMedical educationConstraint-induced movement therapyRehabilitationMedicine

Abstract

fetched live from OpenAlex

Rationale, aims and objectives: Observational movement analysis (OMA) is an integral component of neurological physiotherapy assessment, therefore understanding its related clinical reasoning is an important aspect of clinical practice. Limited knowledge exists on how these skills are developed. Exploring the clinical reasoning associated with OMA in student and expert physiotherapists will inform the development of these critical skills in physiotherapy curricula. The purpose of this research study was to explore OMA and its associated clinical reasoning in student and expert neurological physiotherapists, and to compare and contrast the two groups. Methods: A qualitative interpretive descriptive approach was implemented, using semi-structured online interviews and stimulated recall using eye-gaze behaviour following viewing of a video of a person with stroke performing sit-to-stand. A purposive sampling strategy was used. Interview transcriptions provided the raw data, which was analyzed inductively and independently by two groups of researchers. Results: Five first-year student physiotherapists enrolled in a physiotherapy master’s program and five experienced physiotherapists working clinically in the field of neurorehabilitation with 8-17 years of experience participated voluntarily. Three consistent themes developed from both groups: (I) systematic approach; (II) knowledge; (III) movement performance. Students utilized hypothetico-deductive clinical reasoning while experts combined forward reasoning and reflection-in-action. Conclusion: This study provides insight on the clinical reasoning underlying expert and student OMA. Early clinical exposure incorporating reflective activities will facilitate contextualizing and synthesizing multiple aspects of the clinical presentation. This encourages students to utilize various perspectives of movement, such that the concept of ‘movement diagnoses’ can be facilitated during OMA skill development. Additionally, fostering a philosophy of ‘continuing professional development’ during entry-to-practice physiotherapy programs will promote a lifetime learning approach to physiotherapy practice, essential for optimal patient care. Further investigation is warranted on how forward reasoning can be fostered during OMA in entry-to-practice physiotherapy programs.

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)0.012
metaresearch head score (Gemma)0.030
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.011
Scholarly communication0.0040.002
Open science0.0020.005
Research integrity0.0020.003
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 head0.103
GPT teacher head0.378
Teacher spread0.275 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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