Relationship of employment status and socio‐economic factors with distress levels and counselling outcomes during a recession
Bibliographic record
Abstract
Abstract Background Social inequalities may be magnified during times of economic growth and recession when unemployment levels increase and income opportunities diminish. During a recession with high regional unemployment levels, can therapists expect the same improvements from therapy as they could in good economic times? The aim of this naturalistic study was to use routinely collected outcome measurement data to explore the relationships between unemployment status and client level of distress at the start and completion of counselling. Methods The sample included 20,690 clients from Calgary Counselling Centre (CCC) who received counselling between January 2013 and December 2016, and completed the Outcome Questionnaire‐45.2 (OQ) (Lambert, Gregersen & Burlingame, 2004) at both the first and last sessions. Relationships between employment status and level of distress at first and last counselling sessions for these clients were assessed using cross‐tabulations, chi‐square and one‐way analysis of variance tests of significance. Results Less improvement was gained from counselling during the recession period than during the boom, and outcomes were affected by age, gender and income level differentially for employed and unemployed clients. Discussion Routine outcome data can be utilised at an agency/community level to illustrate the effect of socio‐economic factors on mental health status and treatment outcomes in the general population as well as on community mental health service utilisation. Employment status affects the sociodemographic profile of clients attending a community mental health centre, which in turn affects counselling outcomes overall.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".