Who is successful in career development? A person-centered approach to the study of career orientation profiles
Bibliographic record
Abstract
Purpose This research examined the presence of career orientation profiles by investigating how young workers combined protean career orientation attitudes, motivation to learn to develop one's career and an optimistic future perspective on their career. It explored how a differentiated endorsement of these attitudes and motivation (i.e. career orientation profiles) were associated with the adoption of multiple career-enhancing behaviors, namely proactive career behaviors (i.e. career planning, networking and skill development) and learning behaviors with technologies. Design/methodology/approach Latent profile analysis was conducted among young individuals starting their career (N = 767) and found four distinct profiles. Findings The first profile revealed that 17.2% of workers in this sample were displaying low levels in protean career orientation, motivation to learn and optimistic future time perspective (profile 1). Two differentiated profiles showed either low levels of protean career orientation and high levels of motivation to learn (profile 2) or high levels of protean career attitudes and low levels of motivation to learn (profile 3). These profiles presented an average level of future time perspective and represented 13.8 and 40.6% of the sample. Finally, 28.4% of the sample showed high levels on all these variables (profile 4). Originality/value Only young workers who showed high levels on all these indicators also presented high levels of proactive behaviors and learning with technologies. The other three profiles were associated with suboptimal levels on these outcomes. Taken together, these results offer new insights into the psychological state of mind of workers most adapted to succeed in a modern career.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".