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Record W4410284150 · doi:10.61838/ijbmc.v12i2.950

The Biopsychosocial Model in Modern Healthcare: Overcoming Barriers to Holistic Patient Care

2025· article· en· W4410284150 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational journal of body, mind and culture · 2025
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsnot available
Fundersnot available
KeywordsBiopsychosocial modelHealth careHolistic healthHolistic nursingNursingMedicinePsychologyPsychotherapistAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

The biopsychosocial model has reshaped contemporary healthcare by emphasizing the interconnectedness of biological, psychological, and social factors in health and disease. Unlike the traditional biomedical model, this approach offers a more holistic framework for managing chronic illnesses, pain disorders, and mental health conditions. Despite its well-documented benefits, the model faces significant implementation barriers, including the dominance of reductionist medical education, inadequate interdisciplinary collaboration, and financial constraints in healthcare systems. This editorial highlights the necessity of integrating biopsychosocial principles into clinical practice and discusses strategies to overcome systemic challenges. Key recommendations include revising medical education to incorporate psychosocial training, reforming healthcare policies to support multidisciplinary care, and leveraging digital health technologies to facilitate biopsychosocial interventions. Addressing these obstacles is essential to ensuring patient-centered, effective, and sustainable healthcare systems globally.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.475
Teacher spread0.436 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it