Development of the Inner City attitudinal assessment tool (ICAAT) for learners across Health care professions
Bibliographic record
Abstract
BACKGROUND: Many health professions learners report feeling uncomfortable and underprepared for professional interactions with inner city populations. These learners may hold preconceptions which affect therapeutic relationships and provision of care. Few tools exist to measure learner attitudes towards these populations. This article describes the development and validity evidence behind a new tool measuring health professions learner attitudes toward inner city populations. METHODS: Tool development consisted of four phases: 1) Item identification and generation informed by a scoping review of the literature; 2) Item refinement involving a two stage modified Delphi process with a national multidisciplinary team (n = 8), followed by evaluation of readability and response process validity with a focus group of medical and nursing students (n = 13); 3) Pilot testing with a cohort of medical and nursing students; and 4) Analysis of psychometric properties through factor analysis and reliability. RESULTS: A 36-item online version of the Inner City Attitudinal Assessment Tool (ICAAT) was completed by 214 of 1452 undergraduate students (67.7% from medicine; 32.3% from nursing; response rate 15%). The resulting tool consists of 24 items within a three-factor model - affective, behavioural, and cognitive. Reliability (internal consistency) values using Cronbach alpha were 0.87, 0.82, and 0.82 respectively. The reliability of the whole 24-item ICAAT was 0.90. CONCLUSIONS: The Inner City Attitudinal Assessment Tool (ICAAT) is a novel tool with evidence to support its use in assessing health care learners' attitudes towards caring for inner city populations. This tool has potential to help guide curricula in inner city health.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".