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Record W2494067793 · doi:10.5539/ijps.v8n3p173

Case Studies in Clinical Psychology: Are We Giving up a Publication Type and Methodology in Research on and Teaching of Psychopathology and Psychotherapy?

2016· article· en· W2494067793 on OpenAlexvenueno aff
Dorothea Krampen, Günter Krampen

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPsycINFOPsychologyMEDLINEPsychopathologyObservational studyClinical psychologyPsychotherapistMeta-analysisPsychiatryMedicine

Abstract

fetched live from OpenAlex

<p>Scientometric results on publication trends in clinical psychology, which refer to publication type and methodology of case studies/reports, are presented. Absolute and relative frequencies of clinical case studies are identified for the segment “mental and behavioral disorders” in MEDLINE (ICD-10 Chapter V [F]) as well as for clinical psychology publications documented in PsycINFO and PSYNDEX in 40 publication years (1975-2014). Results show an increase of the absolute number of published case studies documented in MEDLINE and PsycINFO (but not in PSYNDEX), which is highly correlated with the total increase of clinical psychology publications in both databases. Relative frequencies show another picture, namely a drop of the percentage of case studies on mental and behavioral disorders in MEDLINE, and a sharp drop in PSYNDEX since the 1980s. The trend for the relative frequency of case studies within all publications on clinical psychology documented in PsycINFO is V-shaped with 6% in the 1970s, 3% in the early 1990s, and 4-5% after the millennium. Pros and cons of case studies in clinical psychology research and education are discussed. Qualitative and quantitative case study methodologies are distinguished with respect to the phases of clinical trials and observational studies in evidence-based and empirically supported psychotherapy. Subsequently, methodological constraints are balanced with specific values in clinical training, applied research, and innovative research on the symptomatology, etiology, and classification of mental disorders as well as on combined and/or integrative treatment techniques and methods.</p><div> </div>

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.851
GPT teacher head0.717
Teacher spread0.134 · 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 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

Citations5
Published2016
Admission routes1
Has abstractyes

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