A content analysis of verbs and noun phrases used in the AACOM competencies was undertaken. Content analysis allows researchers to draw inferences from texts to capture not only the words themselves but also their contexts.
17 Analysis of this type allows for the identification of thematic elements and other patterns that may exist within a contained document.
18 We categorized the AACOM competencies into relevant cognitive and knowledge dimensions as specified by the taxonomy (
Table 1).
We deployed a 2-step process. First, we classified verbs into the taxonomy's cognitive dimensions: remember, understand, apply, analyze, evaluate, and create. This process involved taking each verb from the competencies and identifying the corresponding category in the taxonomy. When faced with competencies containing multiple verbs, the editors of the taxonomy suggested inferring “which process the teacher intended in order to classify that objective.”
6 Accordingly, we distinguished between nonaction and action verbs. For example, “Identify the patient's chief complaints and appropriately perform a logical physical examination in order to properly diagnose the condition” contains 3 verbs,
identify,
perform, and
diagnose. However, not all 3 verbs function as action verbs for pedagogic purposes.
Identify and
perform are action verbs because they describe actions required to determine student competence.
Diagnose, however, does not drive the cognitive activity with which students are being tasked and so was not coded in our study. A similar approach was taken by and is advocated for by Ven and Chuang.
19
Second, the competencies were categorized into the 4 knowledge dimensions specified by the taxonomy: factual, conceptual, procedural, and metacognitive. Again, in accordance with the taxonomy's guidelines, we analyzed each verb's context as it appears in the competencies.
6 For example, “Identify the association between organ systems, function, and structural findings” has the verb
identify and the context, or noun phrase, of “the association between organ systems, function, and structural findings.” This phrase was most closely associated with Bloom's conceptual knowledge dimension, described as “the interrelationships among the basic elements within a larger structure that enable them to function together.”
6
In cases in which the verb or noun phrase used in the competency was not immediately clear, the taxonomy's deeper descriptions of domains provided further guidance for settling interpretative differences. In instances in which a competency did not fit directly into any dimensions, we coded them as “unclassified” for the cognitive dimension and “unclassified knowledge” for the knowledge dimensions. For example, the competency, “Seek qualified care from a health professional outside the family of the physician” contains the verb seek but cannot be subsumed into the taxonomy because it is a sentence fragment. Although these very few (18 of 978) instances may have been oversights on the part of the competency's authors, for purposes of methodologic consistency, they were coded as unclassified. Similarly, we used the code “unclassified knowledge” in only 1 instance.
Coding was performed collaboratively over multiple working sessions by K.R. and J.M. using the qualitative online research software Dedoose. While the classification of most of the competency's verbs garnered easy consensus among the researchers, in some cases a closer look at context was required. In these cases, following research from Graneheim and Lundman
20 on the role of interpretation in qualitative content analysis, we (K.R. and J.M.) discussed the verbs with a recognition that qualitative content analysis “focuses on the subject and context, and emphasizes differences between and similarities within codes and categories,” while also recognizing that both manifest and latent content is embedded in the language of the competencies. This view is consistent with Finfgeld-Connett's
21 recognition that there is great diversity in approaches to qualitative content, ranging from “impressionistic interpretation to systematic analysis of text-based data,” the latter of which was the aim of the current study.
After initial coding was completed, we undertook an analysis of the data collected by using Dedoose code co-occurrence and application frequency tools. This process, in turn, facilitated further discussion to identify results and notable findings from the data.