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Record W4402495767 · doi:10.1080/03069885.2024.2389845

Career writing interventions for career learning: an integrative literature review

2024· article· en· W4402495767 on OpenAlexaff
Angela Kathleen McNichol, Reinekke Lengelle, Rob F. Poell

Bibliographic record

VenueBritish Journal of Guidance and Counselling · 2024
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPsychological interventionPsychologyCareer PathwaysCareer developmentMedical educationCareer educationPedagogyMedicineVocational education

Abstract

fetched live from OpenAlex

This integrative literature review aimed to synthesise published research on the use of expressive, reflective and creative writing in a career context, known as Career Writing. Studies included English-language research from peer-reviewed journals published between 1990 and 2020 with intervention(s) involving writing for personal and professional development in a career context. Forty-five articles were thematically analysed, and three themes identified: 1. dialogue/voice, 2. learning, and 3. wellness/well-being. Moreover, five subthemes emerged: 2a. professional development, 2b. personal development, 2c. career-identity development, 3a. psychological wellbeing, and 3b. physical health and wellness. Analysis revealed that through dialogue with themselves and others, participants learned personally and professionally and, in some cases, developed insights about career direction. Participants in some of these studies experienced psychological well-being and in four cases they experienced physical improvements. It was emotions in addition to thoughts that led to the most profound insights described in some of the research studies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.042
GPT teacher head0.363
Teacher spread0.321 · 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 designOther design
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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