Basic Analysis of Calls from Suzhou Psychological Aid Hotline from 2010 to 2020
Bibliographic record
Abstract
Objective: Through analyzing the data and content of incoming calls from Suzhou psychological aid hotline from 2010 to 2020, this paper summarizes the hotline service situation and its development trend year by year, and understands the specific situation of hotline work and the demands of hotline callers. Methods: 25356 calls from Suzhou psychological assistance hotline from January 2010 to November 2020 were selected as the research objects. According to the data type, time and basic demographic information, the data were classified. Quantitative statistics and data analysis were used to analyze and study the data by SPSS. Results: 1. The main types of calls were mental and psychological (33.6%), followed by love (13.19%) and marriage and family problems (11.05%). 2. More electricity came from women (12,694 times) than from men (12,662 times); 3. There is seasonal fluctuation in the incoming electricity from the hotline, which is higher in the first and fourth quarters and less in the second and third quarters. Conclusion: Psychological hotline problems mainly focus on mental psychology, love, marriage and family problems, Incoming electricity has gender and seasonal differences and fluctuations, especially gender roles have an impact on the psychological troubles of visitors. Operators need to have professional knowledge in the above aspects and properly use the gender framework to provide psychological counseling and analysis to callers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".