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Record W2075356001 · doi:10.1002/cpp.513

Clinical psychology trainees' research productivity and publications: An initial survey and contributing factors

2007· article· en· W2075356001 on OpenAlexaff
Myra Cooper, Graham Turpin

Bibliographic record

VenueClinical Psychology & Psychotherapy · 2007
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsTrent University
Fundersnot available
KeywordsPsychologyContext (archaeology)ProductivityTheme (computing)SupervisorApplied psychologyMedical educationManagementMedicine

Abstract

fetched live from OpenAlex

Abstract Clinical psychology research productivity is an issue of great professional concern. The current paper explores the rate and nature of trainee publication of research projects in peer‐reviewed journals, together with reasons likely to be relevant to its success and failure. Twenty‐one out of 28 courses responded to a survey composed of closed and open‐ended questions. Data were analysed using inferential statistics and content analysis. Twenty‐four percent of trainees in any one cohort during the period 1999–2004 successfully wrote up their research for publication. Most publications were in psychology journals with modest but respectable impact factors. The most important theme thought to be related to success was supervisor factors, followed by trainee factors, general course factors, study characteristics and the demands of a new job following qualification. Recommendations to enhance publication success are made within the context of a broader discussion of the importance of facilitating the development of the new generation of clinical psychology researchers. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.125
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1250.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
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 head0.816
GPT teacher head0.766
Teacher spread0.049 · 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; both teacher heads agree on what is shown here.

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

Citations34
Published2007
Admission routes1
Has abstractyes

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