Self-reported Subjective Effects of Analytically Confirmed New Psychoactive Substances Consumed by e-Psychonauts: Protocol for a Longitudinal Study Using a New Internet-Based Methodology
Bibliographic record
Abstract
BACKGROUND: During the last few years, the continuous emergence of new psychoactive substances (NPS) has become an important public health challenge. The use of NPS has been rising in two different ways: buying and consuming NPS knowingly and the presence of NPS in traditional drugs as adulterants. The rise of NPS use is increasing the number of different substances in the market to an extent impossible to study with current scientific methodologies. This has caused a remarkable absence of necessary information about newer drug effects on people who use drugs, mental health professionals, and policy makers. Current scientific methodologies have failed to provide enough data in the timeframe when critical decisions must be made, being not only too slow but also too square. Last but not least, they dramatically lack the high resolution of phenomenological details. OBJECTIVE: This study aims to characterize a population of e-psychonauts and the subjective effects of the NPS they used during the study period using a new, internet-based, fast, and inexpensive methodology. This will allow bridging an evidence gap between online surveys, which do not provide substance confirmation, and clinical trials, which are too slow and expensive to keep up with the new substances appearing every week. METHODS: To cover this purpose, we designed a highly personalized, observational longitudinal study methodology. Participants will be recruited from online communities of people who use NPS, and they will be followed online by means of a continuous objective and qualitative evaluation lasting for at least 1 year. In addition, participants will send samples of the substances they intend to use during that period, so they can be analyzed and matched with the effects they report on the questionnaires. RESULTS: The research protocol was approved by the Institutional Review Board of the Hospital del Mar Research Institute on December 11, 2018. Data collection started in August 2019 and was still ongoing when the protocol was submitted (September 2020). The first data collection period of the study ended in October 2020. Data analysis began in November 2020, and it is still ongoing. The authors expect to submit the first results for publication by the end of 2021. A preliminary analysis was conducted when the manuscript was submitted and was reviewed after it was accepted in February 2021. CONCLUSIONS: It is possible to conduct an institutional review board-approved study using this new methodology and collect the expected data. However, the meaning and usefulness of these data are still unknown. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24433.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 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".