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Record W2225331420 · doi:10.1093/ptj/85.3.226

Canadian Physical Therapists' Interest in Web-Based and Computer-Assisted Continuing Education

2005· article· en· W2225331420 on OpenAlexaffabout
Sunita Mathur, Sue Stanton, W. Darlene Reid

Bibliographic record

VenuePhysical Therapy · 2005
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContinuing educationThe InternetMedical educationContinuing medical educationMedicineSpecial Interest GroupSample (material)PsychologyFamily medicineWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Distance education via computer-assisted learning (CAL), including Web-based and CD-ROM learning, confers a number of advantages compared with traditional learning methods. The purposes of this study were (1) to determine the interest of Canadian physical therapists in participating in continuing education using CAL methods and (2) to determine whether interest in CAL was related to type of employment, area of practice, education, computer skill and access, and other demographic variables. SUBJECTS AND METHODS: A random sample of Canadian physical therapists and all members of cardiopulmonary interest groups were surveyed. RESULTS: Of 1,426 survey questionnaires mailed, 69 were returned (58 were unopened and 11 were duplicates). From the remaining 1,357 potential survey responses, 757 responses were received, for an overall response rate of 56%. Seventy-eight percent of the respondents indicated their interest in participating in CAL. Factors associated with interest in CAL included 2 or more hours of Internet access per week, Internet access at both home and work, computer skill, education level, practice area, and belonging to a cardiopulmonary interest group. DISCUSSION AND CONCLUSION: The findings indicate a large positive interest in CAL. Increasing CAL continuing education opportunities could increase options for physical therapists to meet professional expectations for continuing competency.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.457
Teacher spread0.347 · 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 designOther design
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

Citations31
Published2005
Admission routes2
Has abstractyes

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