Trouble with the curve: the 90–9-1 rule to measure volitional participation inequalities among Royal Canadian Mounted Police cadets during training
Bibliographic record
Abstract
Objective: The Royal Canadian Mounted Police (RCMP) Study includes longitudinal multimodal assessments of RCMP cadets from pre-training (i.e., starting the Cadet Training Program [CTP]) to post-deployment and for five years thereafter. The data allow for investigating the multidimensionality of volitional participation in digital health data collection frameworks within serial data collection platforms and the impact of participation inequalities by classifying cadets using the 90-9-1 rule. By classifying cadets as Lurkers, Contributors, and Superusers formally described by the 90-9-1 rule, where 90% of actors do not participate, 9% seldom contribute, and 1% contribute substantially allows for the assessing of relationships between participation inequalities in self-monitoring behaviors as well as whether mental health disorder symptoms at pre-training (i.e., starting the CTP) were associated with subsequent participation. Methods: Participants were asked to complete a Full Assessment prior to their training at CTP, as well as short daily surveys throughout their training. Participation frequency was described using a process where participants were rank ordered by the number of daily surveys completed and classified into one of three categories. Full assessment surveys completed prior to their training at CTP included screening tools for generalized anxiety disorder (GAD), major depressive disorder (MDD), posttraumatic stress disorder (PTSD), alcohol use disorder (AUD), and panic disorder (PD). The Kruskal-Wallis H test was used to assess differences in participation rates between mental health disorder symptom screening groups for each measure at pre-training, and Spearman's Rho was used to test for associations amongst self-reported Full Assessment screening tool responses and the number of daily surveys completed during CTP. Results: =695) and produced 44.4% of daily survey recordings. Lurkers had the largest proportion of positive screens for self-reported mental health disorders at pre-training. Conclusion: The creation of highly individualized, population-based mental health injury programs has been limited by an incomplete understanding of the causal relationships between protective factors and mental health. Disproportionate rates of disengagement from persons who screen positive for mental health disorders further compounds the difficulty in understanding the relationships between training programs and mental health. The current results suggest persons with mental health challenges may be less likely to engage in some forms of proactive mental health training. The current results also provide useful information about participation, adherence, and engagement that can be used to inform evidence-based paradigm shifts in health-related data collection in occupational populations.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".