The Society for Prevention Research 20 Years Later: a Summary of Training Needs
Bibliographic record
Abstract
The Society for Prevention Research (SPR) aims to continually provide relevant professional development training opportunities to advance scientific investigation of ways to improve the health, well-being, and social and educational outcomes of individuals and communities. Our study, led by the Training Needs Assessment Task Force, designed a quantitative questionnaire informed by semistructured, qualitative interviews of 13 key prevention science informants. The questionnaire was deployed to all SPR members, of which 347 completed it. Questions about training topics were asked along 8 categories: (1) theory; (2) preventive interventions; (3) research methods, design, and evaluation; (4) teaching and mentoring; (5) practical and interpersonal skills; (6) communication; (7) project management; and (8) data analysis. Across all categories, respondents reported a high level of interest in receiving training: more than 80% were interested in training in data analytic methods; about 70% indicated interest in theory, preventive interventions, and research methods, design, and evaluation; about 65% were interested in at least 1 communication and project management topic; and 60% showed interest in at least 1 practical and interpersonal skills topic. Training-related interests varied across career level and race/ethnicity, with early-career individuals and people of color typically indicating the most interest. Participants were most likely to endorse self-initiated learning and webinars. SPR preconference training workshops were strongly endorsed for data analysis and preventive intervention topics. Recommendations from our study include a need for SPR to more strongly support self-initiated learning opportunities and continue preconference training programs, with special focuses in statistical methods and preventive interventions and regular assessment of members' training preferences.
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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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".