The barriers and opportunities to support the early career academics and professionals in human factors/ergonomics - revisiting reflections from IEA2015, IEA2018 & IEA2021
Bibliographic record
Abstract
BACKGROUND: The Early-Career Community (ECC) comprises researchers, practitioners, and professionals in their "early-career" stages in the Human Factors/Ergonomics (HFE) profession. Early-career HFE professionals are essential to both current industry decision making and future growth of our profession. OBJECTIVE: This paper provides detailed insights into the barriers and suggestions to support engagement with ECC within the International Ergonomics Association (IEA) and its Federated Societies. METHODS: This report integrates key findings from the formal and informal discussions that occurred with diverse groups of stakeholders (n > 100) at IEA2015, IEA2018 and IEA2021 guided by the participatory inquiry paradigm, cooperative action-inquiry and participatory ergonomics approaches. RESULTS: Barriers to support ECC include: a lack of employment opportunities, poor general awareness and integration of HFE in existing university-courses, financial constraints, inclusivity challenges and a lack of Influence in decision-making. While some of the more systemic challenges are context-specific and cannot be overcome, ECCs suggested that: the IEA and its Federated Societies include ECC members as part of their boards; a Standing Committee for the ECCs be established as part of the IEA; make use of social-media more effectively to engage the ECC. More mentorship, networking, knowledge sharing, training and education, combined with financial-support will ensure that the ECC can participate. CONCLUSION: ECC members experience complex and dynamic challenges that affect their development and involvement in the broader HFE profession. It is therefore critical that appropriate, global, national and local strategies are developed to continue to support and develop the ECC to ensure the continued growth of and demand for HFE.
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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 | 0.084 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".