Evaluating a helpline for post‐secondary students: Caller distress, ability to face concern and satisfaction with helpline
Bibliographic record
Abstract
Abstract Background Helplines for post‐secondary students have emerged in the last decade to address the growing mental health needs and increasing help‐seeking of this population in ways that are responsive to their needs and preferences. However, there is no publicly available evidence of the effectiveness of helplines for post‐secondary students. Aims This study evaluated the outcomes of Good2Talk, a helpline for post‐secondary students in Ontario, Canada, that offers professional counselling and information and referral services related to mental health, addictions and well‐being. Methods In this cross‐sectional study, purposive sampling was used to recruit post‐secondary students who contacted Good2Talk between March 2016 and March 2020. Data were collected using post‐call questionnaires. Paired samples t‐tests and multinomial logistic regression analyses were used to analyse the data from 619 post‐secondary students. Results Participants reported significant decreases in distress and increases in their ability to face their concern after contacting the helpline. Feeling understood and low pre‐call distress were significant predictors of low post‐call distress. Confidence in their abilities and having a better plan were significant predictors of high post‐call ability to face their concern. Age, gender and number of previous calls to the helpline were not significant predictors of positive outcomes. Most participants reported that they would recontact the helpline and recommend the helpline to a peer. Conclusion The study indicates that counselling and information and referral services can be effective in reducing distress and increasing post‐secondary students' abilities to address their mental health concerns.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".