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Record W2744752986 · doi:10.7728/0403201305

Implications of Community-Based Research for Professional Psychology Training: Reflections from Two Early Career Psychologists

2017· article· en· W2744752986 on OpenAlexafffund
Melissa Tiessen, Julie Beaulac

Bibliographic record

VenueGlobal Journal of Community Psychology Practice · 2017
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of OttawaCanadian Psychological AssociationMcGill University
FundersInstitute of Aboriginal Peoples HealthSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsProfessional psychologyApplied psychologyPsychologyCommunity psychologyTraining (meteorology)PedagogySocial psychologySociologyMedical educationClinical psychologyMedicineBurnout

Abstract

fetched live from OpenAlex

Community psychology (CP) has valuable philosophical perspectives and methodological approaches to offer the wider discipline of psychology, yet it remains underappreciated and often invisible in most professional training programs in psychology, including those programs intended to train in the areas of clinical, counselling, school, and neuropsychology. Community-based research (CBR) is one particular methodological approach within CP that has the potential to enhance standard research training experiences, as well as to enhance professional psychology training more generally. In this paper, we discuss the professional psychology training implications of CBR approaches, highlighting potential changes to the existing training structure that could facilitate wider access to training in CBR, and thereby enhance the competencies of professional psychologists. We also critically reflect on our experiences conducting our own CBR dissertation projects while becoming trained as clinical psychologists. We encourage other trainees, professional psychologists, and training programs to consider the merits of incorporating CP perspectives and approaches into their work.

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)0.026
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0150.001
Scholarly communication0.0000.001
Open science0.0050.001
Research integrity0.0010.013
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 head0.798
GPT teacher head0.712
Teacher spread0.087 · 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 designNot applicable
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

Citations0
Published2017
Admission routes2
Has abstractyes

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