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Record W2264234548 · doi:10.82396/cjcd.v15i1.3097

Examining the Career Engagement of Canadian Career Development Practitioners

2021· article· en· W2264234548 on OpenAlexaboutno aff
Deirdre A. Pickerell

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsCareer developmentWork engagementSample (material)PsychologyProductivityPublic relationsMedical educationWork (physics)Applied psychologySocial psychologyPolitical scienceMedicineEngineering

Abstract

fetched live from OpenAlex

The purpose of this research was to examine the career engagement of Canadian Career Development Practitioners (CDPs), a group of professionals tasked with helping Canadians with career and employment-related concerns. Previous studies with this participant sample have not focused on engagement, which is considered to be an important metric for worker satisfaction and productivity. As a result, this study established an important foundation for ongoing work. A mixed-method approach, using the newly developed quantitative measure of career engagement supported by some qualitative questions, was used for the study. Findings indicate that, overall, Canadian CDPs are engaged with their careers; however, the sector’s youngest and newest as well as oldest and most senior workers are least likely to be engaged. Although this study produced meaningful results, more research is needed. A larger sample size, with better geographical representation would help confirm workers most at risk for lower engagement. In addition, it is likely important to identify whether CDPs with lower engagement levels are at risk of providing a poorer quality of service to clients and, perhaps, subsequently impacting a client’s ability to be successful.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.144
GPT teacher head0.317
Teacher spread0.173 · 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 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland)Same topicRetirement, Disability, and EmploymentFrench-language works237,207