Contemporary challenges, needs and opportunities for emerging behavioral nutrition and physical activity researchers: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Emerging researchers commonly navigate challenging and insecure working environments. Yet the impact on emerging behavioral nutrition and physical activity researchers is unknown. Hence, we sought to identify the contemporary challenges, needs, and opportunities for emerging behavioral nutrition and physical activity researchers. METHODS: We employed a convergent mixed methods design, using an online survey. Participants completed socio-demographic questions, and rated the impact of personal and professional challenges, development needs with descriptive elaborations, and existing and desired professional development opportunities. Data analysis included thematic analysis of open-ended responses and descriptive statistics and multiple linear regressions of quantitative data. Integration of quantitative and qualitative data was through narrative and weaving. RESULTS: Emerging researchers (n = 111, 57% graduate students) from over 20 countries participated. Synthesised results related to all four domains of the Researcher Development Framework. Specifically, we identified 8 themes relating to conducting research (domain 1); physical and mental health, and networking (domain 2); grant funding, and employment opportunities (domain 3); and leadership, supportive work networks, and communication with non-academic audiences (domain 4). Financial comfort was a predictor of both professional and personal development needs. CONCLUSIONS: Our study highlights the multiple challenges emerging researchers face, with increasing demands of collective efforts to support sustainable career development. Our findings serve as a foundation for promoting an inclusive and equitable research environment for emerging researchers. Though individual-level solutions may help, greater impact is likely from systemic changes to increase job security, career progression pathways and availability of ECR-specific funding.
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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 | 0.062 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".