Original research · 2026-07 edition

AI SEO Statistics: Womens Rehab Center (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in womens rehab center.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

Why is a women-only rehab center considered more effective for trauma recovery than a co-ed facility?
What are the average monthly costs for an inpatient women's treatment center if I don't have private insurance?
I'm a single mother needing addiction help; are there facilities that allow my children to stay with me during treatment?
How do I know if my sister needs a medically supervised detox or if she can go straight to a residential women's program?
What specific questions should I ask a facility to ensure they use evidence-based practices for dual diagnosis in women?
Are there any red flags I should look for when touring a female-focused recovery center?
Is it better to send my daughter to a rehab center close to home or should she go out of state to get away from her environment?
How can I tell if a women's rehab center is actually trauma-informed or if it's just a marketing buzzword they use?
Show all 15 questions
My wife is struggling with alcohol but won't admit it; how do I find an interventionist who specializes in working with women?
What are the pros and cons of a holistic women's rehab versus a traditional clinical 12-step program?
Can I use my HSA or FSA funds to pay for a residential women's treatment program?
I need a facility that handles both eating disorders and substance abuse specifically for women; how do I find a dual-accredited center?
What is the typical success rate for women-only outpatient programs compared to inpatient ones for opioid addiction?
My friend needs a bed today for a female-only detox; how do I find out who has immediate availability in the tri-state area?
Do women's rehab centers typically offer job placement assistance or transitional housing after the initial 30-day program?

Model by model

24-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about womens rehab center buyers.

Behavior rates across 15 womens rehab center buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional53%33%40%47%
Suggests DIY first27%7%7%73%
Names specific providers0%27%7%67%
Gives price or cost info7%27%7%80%
Tells to check reviews27%13%0%73%
Tells to verify credentials53%40%13%53%
Mentions case studies / portfolio7%0%0%93%
Mentions local proximity40%40%20%40%
Gives selection criteria80%67%47%40%
Warns about red flags27%20%20%80%
Asks a clarifying question67%60%0%20%
Recommends multiple quotes7%0%0%93%

By model

How each assistant handled Womens Rehab Center questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same womens rehab center questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 53.3% (ChatGPT) down to 33.3% (Claude), a 20-point gap on an identical question set.

Across the 15 womens rehab center answers it produced, ChatGPT recommended hiring a professional in 53.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 53.3%, averaging 592 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 80% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 womens rehab center answers it produced, Claude recommended hiring a professional in 33.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 26.7% of answers (about 0.5 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 40%, averaging 300 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 womens rehab center answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 6.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 231 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a womens rehab center buyer to a professional (53.3%) and Claude the least (33.3%). ChatGPT produced the longest answers, at 592 words on average. Specific providers were named most often by Claude (26.7%) — even there, roughly one answer in 4 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 24.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a womens rehab center buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 66.7% (ChatGPT) — a 67-point spread.
  • Tells the buyer to verify credentials: from 13.3% (Gemini) to 53.3% (ChatGPT) — a 40-point spread.
  • Gives selection criteria: from 46.7% (Gemini) to 80% (ChatGPT) — a 33-point spread.
  • Names a specific provider: from 0% (ChatGPT) to 26.7% (Claude) — a 27-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.

The widest single gap — asks a clarifying question, 67 points — means a womens rehab center buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the womens rehab center market.

Where they agree

The points of near-consensus in Womens Rehab Center.

On other behaviors the three models move almost in lockstep — the points of near-consensus for womens rehab center, where all three landed within a few points of each other:

  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
  • Warns about red flags or scams: 20%–26.7% across all three (a 7-point spread).
  • Recommends multiple quotes: 0%–6.7% across all three (a 7-point spread).
  • Recommends hiring a professional: 33.3%–53.3% across all three (a 20-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

All twelve coded behaviors for Womens Rehab Center, averaged across the three models.

The behaviors AI models reproduce most often for womens rehab center are gives selection criteria (64.5% on average), recommends hiring a professional (42.2%) and asks a clarifying question (42.2%); the rarest are recommends multiple quotes (2.2%), mentions case studies or portfolio (2.2%) and names a specific provider (11.1%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 64.5% on average (ChatGPT 80%, Claude 66.7%, Gemini 46.7%) — a 33-point spread.
  • Recommends hiring a professional: 42.2% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 40%) — a 20-point spread.
  • Asks a clarifying question: 42.2% on average (ChatGPT 66.7%, Claude 60%, Gemini 0%) — a 67-point spread.
  • Tells the buyer to verify credentials: 35.5% on average (ChatGPT 53.3%, Claude 40%, Gemini 13.3%) — a 40-point spread.
  • Mentions local proximity: 33.3% on average (ChatGPT 40%, Claude 40%, Gemini 20%) — a 20-point spread.
  • Warns about red flags or scams: 22.2% on average (ChatGPT 26.7%, Claude 20%, Gemini 20%) — a 7-point spread.
  • Suggests a DIY approach first: 13.4% on average (ChatGPT 26.7%, Claude 6.7%, Gemini 6.7%) — a 20-point spread.
  • Gives price or cost information: 13.4% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Tells the buyer to check reviews: 13.3% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 0%) — a 27-point spread.
  • Names a specific provider: 11.1% on average (ChatGPT 0%, Claude 26.7%, Gemini 6.7%) — a 27-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Recommends multiple quotes: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the womens rehab center buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the womens rehab center buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 13.3% of answers on average. Verifying credentials or certifications appeared in 35.5%. Warning about red flags or scams appeared in 22.2%.

On structuring the decision, a selection-criteria checklist showed up in 64.5% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for womens rehab center is "recommends multiple quotes" at 2.2% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Womens Rehab Center providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 womens rehab center answers, a specific provider was named in 11.1% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for womens rehab center: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Womens Rehab Center questions cover.

The 15 questions behind every percentage on this page were drawn from real womens rehab center (healthcare services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact womens rehab center question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific womens rehab center question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →