Physician Assessment of Social Determinants of Health: A Necessary Component in Improving Care of Patients
Bibliographic record
Abstract
The importance of building a therapeutic relationship between a physician and a patient is taught early on in a medical student's training, specifically through the practice of obtaining a patient history. This process consists of gathering information in four main categories: the history of the present illness, personal social history, past medical history, and family history. Each piece of information obtained within these categories is vital in ensuring a patient receives appropriate and effective care. Specifically, a social history consists of asking about a patient's relationship status, support system, home environment, interests, exercise, nutritional habits, substance use, and sexual history. To complete a successful and full social history, one should try to address the social determinants of health. As per the Government of Canada’s website, social determinants of health “refer to a specific group of social and economic factors within the broader determinants of health. These relate to an individual’s place in society such as income, education or employment” [1]. Consequently, a critical component of a complete social history interview should be investigating a patients socioeconomic status. Low socioeconomic status (LSES) has been found to play a role in incidence and susceptibility to a variety of health conditions. As such, I believe that screening for and asking questions pertaining to the socioeconomic status of a patient should be considered a vital and essential component of every patient assessment.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".