The Use of Text Messaging for Peer Support Among Counselling Psychology Graduate Students
Bibliographic record
Abstract
The journey through graduate school to become a counselling psychologist is inherently challenging. Consequently, many students face emotional stress. Peers offer a unique source of support through a shared understanding of their experience. This pilot study used focused ethnography to understand how counselling psychology graduate students engage in emotional support with a peer through the use of text messaging within a naturalistic context. A three-member peer support group, comprising the researchers, served as the convenience sample. In this manner, the researchers both took part in and analyzed the experience of text messaging based peer support. Transcripts of emotional peer support interactions were obtained through sampling the participant-observers’ naturally occurring text message conversations. Elements of Braun and Clarke’s thematic analysis (TA) and Elo and Kyngas’ content analysis (CA) were used to categorize the raw data. The main findings indicate peer support bilaterally encompasses action, connection, disclosure, hearing, initiation, shared happiness, and solidarity. Within a support conversation, supporters predominantly used connection statements, whereas supportees mainly utilized emotional disclosure. These preliminary findings suggest that text messaging offers an immediate, intimate, and readily available platform through which peers can actively create a supportive dialogue.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".