Identifying the challenges of online education from the perspective of University of Medical Sciences Students in the COVID-19 pandemic: a Q-methodology-based study | BMC Medical Education

This cross-sectional review was performed employing the Q methodology in the course of the pursuing 6 techniques using Barry and Proops system [19].

Phase 1 and 2: defining the concourse

At this stage, a concourse area was shaped with the identification of the subject or thought of the review. The offered views on the issue raised for the concourse can be formed from a review of texts and industry experts in this discipline [19].

In this research, the matter and strategy for the concourse were the difficulties of on the net education and learning in the course of the COVID-19 pandemic. The concourse bundled a collection of varied materials related to the exploration matter that was mentioned between learners. The learners (P-established) who also experienced contributed earlier to the growth of the initial established of statements. 30-1 learners participated in semi-structured interviews, and we experimented with to discover their subjectivity about the investigation subject applying the Q method [20].

In this examine, the concourse (sample of people today) bundled pupils of the University of Medical Sciences (paramedical pupils) who had enough details about on-line instruction during the COVID-19 pandemic.

Phase 3: screening and choice of statements (Q-sample)

In the course of the semi-structured interviews with 31 learners, 70 statements have been extracted about the perceived difficulties of on the net schooling. The Q merchandise ended up selected extremely carefully so that merchandise did not overlap, and at the very same time, no standpoint must be missing. Hence, the choice method requires the most time and hard work of all the techniques of the Q methodology. Thus, analysis team removed equivalent unrelated, and ambiguous statements from the Q set. Last but not least, 50 statements were being selected.

Stage 4: selected P-established

Learners who participated in the concourse (interviews) ended up chosen as a sample of folks to take part in sorting in the Q review (P-set). In the current examine, students had been chosen by purposive sampling to include students who had an instructional, experienced, experimental connection or former awareness about the topic of review. This collection of samples built the contributors with additional diverse mentalities enter the study. It is proposed that in Q research, the selection of members to sort statements ought to be less than the variety of statements all around the research matter [21]. In the existing examine, the number of participants who ranked the problems of on the web education and learning plans was 31 (Table 1).

Stage 5: Q-sort

At this stage, the ordinary distribution table in the form of a Likert scale from − 5 to + 5 was made offline. Guidelines on distributing the expressions on the typical distribution table have been supplied. In the very first stage, the function of the review is the amount of statements selected by means of the job interview. In the next phase, put the statements in three columns: “I agree”, “I have no opinion,” and “I disagree. In the 3rd stage, the statements (obligatory) are distributed in the standard Likert distribution diagram (− 5 to 5+), describing the motive for choosing the two finishes of the Likert scale from their issue of view and finally moving into the demographic facts. Therefore, in Q, the sorting process is subjective [19]. In other words and phrases, sorting merchandise in the regular distribution allow for just about every participant to current their internal point of view by way of sorting.

Phase 6: evaluation and interpretation of things

Students’ data obtained from Q sorting ended up entered into PQ-Method computer software edition 2.35. The approach of assessment and interpretation was performed in 3 phases: (a) identification of aspects, (b) conversion of elements into element arrays (c) interpretation of factors applying issue arrays.

1. A)

   Factor Identification

The extraction of variables in PQ-Technique software was completed by way of the pursuing sequential ways: (a) principal ingredient analysis, (b) identification of latent variables, (c) varimax rotation and evaluation of loading factors for specific values higher than 1.00, d) estimation of the percentage of variance discussed by the determined things and (e) differentiation of interpretable elements with at minimum two correlated Q forms [22].

1. B)

   Convert component to element arrays

The correlation among each and every Q sort and a person identified variable indicates the degree of conversation amongst the Q kinds and the discovered variables [19, 23]. The handbook flagging in PQ-Approach software was applied for this examine. The correlation coefficients of at minimum .364 have been thought of as the slice-off stage (the absolute value of the variable load is increased than ((frac2.58sqrtN)). That aspect load was 99{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f} significant, respectively, and the worth of N was equivalent to the selection of Q statements (N = 50). Sorted for recognized aspects [24]. Specs specified on a aspect are applied to build a element array for that issue. The component array signifies the sorting of that element (position of look at) applying z-scores. The issue array for every single variable decided the diploma to which each and every expression was in the spectrum, so a a lot more correct interpretation of each individual issue (subjectivity) was attained according to the placement of every expression. (P-value< 0.05 vs. 0.01) is also determined from the Z score to distinguish expressions [25].

1. III)

   Factor interpretation using factor arrays

Distinct Q expressions were identified, and factors were interpreted textually. The defining expressions for a factor were those that had a rating value of “+ 5”, “+ 4”, “4-,” 5- “in factor arrays that had different scores (P < 0.05) in a given factor Compared to their scores on other factors, the post-P-set interview was conducted at the end of Q sorting to confirm the diagnosis and interpretation of item subgroups among the identified factors.