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Record W2099237267 · doi:10.1177/0011000011417145

Voices of Early Career Psychologists in Division 17, the Society of Counseling Psychology

2011· article· en· W2099237267 on OpenAlexaff
Nathan Grant Smith, Briana K. Keller, Debra Mollen, Meredith L. Bledsoe, Larisa Buhin, Lisa M. Edwards, Jacob J. Levy, Jeana L. Magyar‐Moe, Oksana Yakushko

Bibliographic record

VenueThe Counseling Psychologist · 2011
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsMcGill University
Fundersnot available
KeywordsMentorshipPsychologyDiversity (politics)Counseling psychologyCareer developmentCareer counselingPositive psychologyMedical educationApplied psychologySocial psychologySociologyMedicine

Abstract

fetched live from OpenAlex

This article reports on a survey of early career members of the Society of Counseling Psychology (SCP). Seventy early career psychologists completed a survey assessing the usefulness and climate of SCP, barriers to and facilitative factors for involvement in SCP, inclusiveness of SCP regarding cultural diversity and professional interests, degree of involvement in various aspects of SCP, and their areas of satisfaction and dissatisfaction with SCP membership. In general, participants were split on the degree to which they were satisfied with SCP, with participants in faculty positions reporting significantly more positive views of SCP than their practitioner counterparts did. Faculty members viewed SCP as more useful to their careers and reported more positive social interactions within SCP than did non–faculty members. Open-ended responses suggested that satisfaction with SCP was related to availability of mentorship and opportunities for involvement in SCP. Suggestions for engaging new professionals in SCP are offered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0060.003
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.119
GPT teacher head0.377
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations21
Published2011
Admission routes1
Has abstractyes

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