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Record W1991017300 · doi:10.1177/030802260506800205

Publication Practices of English Language Occupational Therapy Journals

2005· article· en· W1991017300 on OpenAlexaff
Ted Brown, Sylvia Rodger, Anita Brown

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2005
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsPublishingOccupational therapyEnglish languageVariety (cybernetics)MedicineMedical educationPsychologyImpact factorInterpretation (philosophy)Family medicinePolitical sciencePsychiatryLinguisticsMathematics educationComputer science

Abstract

fetched live from OpenAlex

The aim of this study was to determine the characteristics and publication practices of English language occupational therapy journals. An adapted version of the Survey of Editors Regarding Publishing Practices was posted to the editors of English language occupational therapy journals (n = 14) in September 2001. The response rate was 92.9% (n = 13). Journals were published on average 4.77 times per year and comprised 89% text and 10% advertising. The preferred average length of manuscripts was 20 pages using American Psychological Association format. The average acceptance rate of unsolicited manuscripts was 46.6%. All the journals were peer-reviewed publications and 80% were research oriented. The most frequently cited reasons for rejection of manuscripts were methodology problems, poorly developed idea, poorly written and data interpretation problems. The professional focus of published manuscripts was on research and the clinical foci were on paediatrics, gerontology and physical medicine. The study concludes that there are a variety of publishing opportunities available to occupational therapists. It is essential that prospective authors consult the journal guidelines for authors, including the types of manuscript accepted.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.272
GPT teacher head0.541
Teacher spread0.269 · 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

Citations12
Published2005
Admission routes1
Has abstractyes

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