The integrated behavioral model of mental health help seeking (IBM-HS): A health services utilization theory of planned behavior for accessing care.
Bibliographic record
Abstract
This article introduces the integrated behavioral model of mental health help seeking (IBM-HS), a theoretical model for understanding the constructs (e.g., systemic, predisposing, and enabling factors; mental health literacy; illness perceptions; perceived need; stigma; shame; perceived benefits, motivation) that influence people's decision making around seeking professional mental health care and their ultimate access to formal treatment. The IBM-HS is a help-seeking-specific adaptation of the empirically supported integrated behavioral model and integrative model, which are themselves evolutions of the theory of planned behavior and theory of reasoned action. The IBM-HS posits that help-seeking determinants (e.g., structural forces; cultural influences; past help-seeking experience; evaluated need; mental health perceptions, knowledge, and skills; social support) influence help-seeking beliefs (i.e., outcome beliefs, experiential beliefs, beliefs about others' expectations, beliefs about others' behavior, logistical beliefs), which in turn determine their respective help-seeking mechanisms (i.e., attitude, perceived norm, personal agency). These mechanisms collectively influence help-seeking intention, which drives prospective help-seeking behavior, subject to the moderating effects of determinants. Finally, prospective behavior has reciprocal feedback loop effects on certain determinants and beliefs. This article describes the need for the IBM-HS, the model's constructs and their interrelations, measurement considerations, and how the model can be used by scholarly and applied users to systematically understand people's intention to seek professional mental health care services and what helps or hinders them from utilizing this care. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".