Bibliographic record
Abstract
Cannabis use among teenagers in Canada is a concern because of the long-term and irreversible effects cannabis has on the developing body and mind. Nurses can be instrumental in screening for cannabis abuse by implementing a tool to assess for substance use disorder (SUD) and triage drug users to appropriate treatment. This project focused on how to implement the CRAFFT screening tool while gaining insight of the practitioner's knowledge base about the tool and how SUD is being screened for, currently. The CRAFFT screening tool aligns with the DSM-IV's SUD diagnosis criteria, allowing for efficient identification of those at risk for SUDs. Rotter's social-behavioural learning theory is presented to provide a greater understanding of how one's environment affects SUDs. Sources of evidence were primary health care providers (N = 10) at the health centre where this project was conducted. Data were collected before and after the participants engaged in the learning module on the CRAFFT screening tool. A descriptive analysis found that being acquainted with the tool allowed health care providers to understand the significance of screening for cannabis use among young adults and teenagers and to have more detailed documentation of patients' relationships with cannabis. The screening tool was favoured by 90% of the participants for cannabis use assessment after learning about the tool with this project. Nine out of ten of the participants indicated that they will now use the tool to aide in identifying SUD. Once SUD has been identified with the use of the CRAFFT screening tool, 80% of the participants indicated that they would refer their patients for further assessment and treatment for this substance abuse, which would promote positive social change.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".