Insights into Career Human Agency: A Look at the Experiences of Newly Trained Mental Health Professionals During a Global Pandemic
Bibliographic record
Abstract
The present study explored the career experiences of newly trained mental health professionals in the context of the COVID-19 pandemic. Career experiences were examined using a new meta-theory titled Career Human Agency Theory (CHAT) and its four components: career intentionality, career forethought, career self-reactiveness, and career self-reflectiveness. CHAT is an emerging career psychology theory and is therefore in need of research to verify, support, and expand the theory. Empirical insights into how career human agency is demonstrated, particularly during times of disruption and uncertainty, remains underexplored. This study illuminated how human agency was evidenced in the career experiences of mental health professionals who graduated and entered their field of work amid the pandemic. Ten newly trained mental health professionals shared their stories through semi-structured interviews. The data was analyzed using Interpretive Phenomenological Analysis (IPA). Findings revealed the following themes within each core component of career human agency: career intentionality was influenced by both internal and external driving forces; career forethought was impacted by senses of self-efficacy in meeting the new demands of the virtual world, job uncertainties, and new career paths; career self-reactiveness was undertaken by simultaneously relying on intrapersonal adaptation and interpersonal relationships; and career self-reflectiveness outlined positive aspects and disappointments along the career journey. A key theme that emerged throughout was the importance of relationships, personal and professional, that support individual human agency, especially during challenging times. Theoretical implications include a proposed addition of relational dynamics to the CHAT model. Practical implications for career counselling and higher education are discussed.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| 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".