Demographic on Help-Seeking between People based on Use of (Mental) Healthcare
Bibliographic record
Abstract
Help-seeking behavior involves is the combination of help and seeking, but this is not a straightforward concept, and there are multiple barriers involved in an individual seeking help that depend on the need an individual is seeking help to resolve and other individual characteristics about any given individual. There are many barriers that preclude help-seeking behavior, and the purpose of this paper is to look at demographic barriers that may encourage or inhibit help-seeking behaviors. A sample of data from the CDC Pulse Survey between the dates of March 17th, 2021 and March 29th, 2021 were utilized for this study. Information was gathered regarding psychological symptom presentation, use of healthcare services, insurance status, and whether they accessed (mental) healthcare. The data was transformed from frequency data into nominal data that indicated the presence or absence of any one condition. Chi Squared analyses were utilized to identify how each demographic group differentiated within each construct, and correlations were utilized within broad constructs to differentiate if individuals were significantly different from each other. These results demonstrated demographic differences between individuals and how that predicts help-seeking for both medical and psychological care as well as symptom presentation and insurance coverage and significant differences within those groups. The results inform a general standard of care as it relates to different demographic groups and have implications around which treatment procedures would be best applied to which groups of people.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".