Assessing the Acceptability and Feasibility of a Web-Based Screening for Psychoactive Substance Users Among a French Sample of University Students and Workers: Mixed Methods Prospective Study
Bibliographic record
Abstract
BACKGROUND: Early detection in the prevention of addictive behaviors remains a complex question in practice for most first-line health care workers (HCWs). Several prevention measures have successfully included a screening stage followed by a brief intervention in case of risk-related use or referral to an addiction center for problematic use. Whereas early detection is highly recommended by the World Health Organization, it is not usually performed in practice. OBJECTIVE: The aim of this study was to assess the acceptability and feasibility of a web-based app, called Pulsio Santé, for health service users and first-line prevention HCW and to carry out an exhaustive process of early detection of psychoactive substance use behaviors. METHODS: A mixed methods prospective study was conducted in 2 departments: HCWs from the regional occupational health department and from the university department of preventive medicine dedicated to students were invited to participate. Participants 18 years or older who had been seen in 2017 by a HCW from one of the departments were eligible. The study procedure comprised 5 phases: (1) inclusion of the participants after a face-to-face consultation with an HCW; (2) reception of a text message by participants on their smartphone or by email; (3) self-assessment by participants regarding their substance use with the Pulsio Santé app; (4) if participants agreed, transfer of the results to the HCW; and (5) if participants declined, a message to invite them to get in touch with their general practitioner should the assessment detect a risk. Several feasibility and acceptability criteria were assessed by an analysis of a focus group with the HCW that explored 4 themes (usefulness and advantages, problems and limitations, possible improvements, and finally, integration into routine practice). RESULTS: A total of 1474 people were asked to participate, with 42 HCWs being involved. The percentage of people who agreed to receive a text message or an email, which was considered as the first level of acceptability, was 79.17% (1167/1474). The percentage of participants who clicked on the self-assessment link, considered as the second level of acceptability, was 60.24% (703/1167). The percentage of participants who completed their self-evaluation entirely, which was considered as the first level of feasibility, was 76.24% (536/703). The percentage of participants who shared the results of their evaluation with the HCWs, considered as the second level of feasibility, was 79.48% (426/536). The qualitative study showed that there were obstacles on the side of HCWs in carrying out the recommended interventions for people at risk based on their online screening, such as previous training or adaptations in accordance with specific populations. CONCLUSIONS: Quantitative results showed good acceptability and feasibility of the Pulsio Santé app by users and HCWs. There is a need for further studies more directly focused on the limitations highlighted by the qualitative results.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".