The Reasons Behind a Career Change Through Vocational Education and Training
Bibliographic record
Abstract
We report the results of qualitative research on adults who enrolled in a vocational and education training (VET) program with the intention of changing their careers. The participants were 30 adults aged between 25 and 45 years. A modified version of the consensual qualitative research method was applied to transcriptions of semi-structured interviews with the participants. There appeared to be two main reasons underlying the decision to enrol in a VET program with the aim of initiating a career change. Based on the reasons given, two groups (career changers and proactive changers) and five distinct categories were recognized. The career changers included individuals who wished to change careers due to dissatisfaction with their current situation. In this group, the decisions were motivated by either health problems or personal dissatisfaction. The proactive changers included individuals who wished to reorient their career because of a desire to undertake new projects. In this group, there were three categories of reasons: a wish to attain better working conditions, a search for personal growth and a desire to have an occupation that fitted the person’s vocation. Thus, the participants reoriented their careers according to various motivations, pointing to the existence of a heterogeneous population and the complexity of the phenomenon. The results highlight the importance of understanding the subjective reasons behind career changes and the need to adjust career interventions accordingly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".