Reactivity to Daily Self-Monitoring of Cannabis Use in Biological Females
Bibliographic record
Abstract
Assessment reactivity involves changes to behaviours from self-monitoring those behaviours (Nelson & Hayes, 1981). In the substance use field, reactivity has been identified both as a potential confound in daily diary research (Cohn et al., 2015) and as a possible intervention tool in clinical practice (Cohn et al., 2018). Reactivity to daily self-monitoring of alcohol and tobacco use has been inconsistent in prior research. Reactivity to daily self-monitoring of cannabis use quantity has received far less study. This study involved secondary analyses of data from N = 88 females who self-monitored their cannabis use for 32 days. We examined objective reactivity of cannabis use to daily self-monitoring by assessing changes in daily cannabis use over 32 days. We also explored participants’ perceptions of the impact daily self monitoring had on their cannabis use at study completion (i.e., subjective reactivity). In hurdle models testing objective reactivity, neither probability of cannabis use, nor quantity of cannabis use, changed significantly over the study period. Many respondents (45%) reported no subjective reactivity, though a slight majority (55%) reported some subjective reactivity. Subjective reactivity did not moderate objective reactivity over time; however, higher subjective reactivity was significantly associated with increased variability (interquartile range [IQR]) in cannabis use across the self-monitoring period. Overall, reactivity appears unlikely to confound research utilizing daily diary cannabis measures, and daily self-monitoring of cannabis use may be unlikely to serve as a useful stand-alone intervention for reducing cannabis use in non-treatment-seeking individuals. Potential clinical implications of the novel finding of a link between subjective reactivity and objective cannabis use variability 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".